Translating 3D Slicer to Latin America Language: a Method for Making a Medical Open-Source Software Widely Available

Translating 3D Slicer to Latin America Language: a Method for Making a Medical Open-Source Software Widely Available | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Translating 3D Slicer to Latin America Language: a Method for Making a Medical Open-Source Software Widely Available View ORCID Profile Paulo Eduardo de Barros Veiga , View ORCID Profile Luiz Otavio Murta Junior , Douglas Samuel Gonçalves , Lucas Sanchez Silva , Víctor M. Montaño-Serrano , Enrique Hernandez-Laredo , Valeria Gómez-Valdes , Monserrat Ríos-Hernández , View ORCID Profile Andras Lasso , View ORCID Profile Steve Pieper , Adriana Vilchis-González , View ORCID Profile Sonia Pujol doi: https://doi.org/10.1101/2025.09.15.25335771 Paulo Eduardo de Barros Veiga 1 Department of Computing and Mathematics, University of São Paulo , Ribeirão Preto, São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paulo Eduardo de Barros Veiga For correspondence: pauloveiga{at}usp.br Luiz Otavio Murta Junior 1 Department of Computing and Mathematics, University of São Paulo , Ribeirão Preto, São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luiz Otavio Murta Junior Douglas Samuel Gonçalves 1 Department of Computing and Mathematics, University of São Paulo , Ribeirão Preto, São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lucas Sanchez Silva 1 Department of Computing and Mathematics, University of São Paulo , Ribeirão Preto, São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Víctor M. Montaño-Serrano 2 Universidad Autónoma del Estado de México , Toluca, México Find this author on Google Scholar Find this author on PubMed Search for this author on this site Enrique Hernandez-Laredo 2 Universidad Autónoma del Estado de México , Toluca, México Find this author on Google Scholar Find this author on PubMed Search for this author on this site Valeria Gómez-Valdes 2 Universidad Autónoma del Estado de México , Toluca, México Find this author on Google Scholar Find this author on PubMed Search for this author on this site Monserrat Ríos-Hernández 3 Queen’s University , Kingston, ON, Canada 2 Universidad Autónoma del Estado de México , Toluca, México Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andras Lasso 3 Queen’s University , Kingston, ON, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andras Lasso Steve Pieper 4 Isomics Inc. , Cambridge, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Steve Pieper Adriana Vilchis-González 2 Universidad Autónoma del Estado de México , Toluca, México Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sonia Pujol 5 Brigham and Women’s Hospital, Harvard Medical School , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sonia Pujol Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract 3D Slicer, an open-source software platform for the analysis and 3D visualization of medical imaging data, aims to make cutting-edge research tools available to clinical researchers and scientists worldwide. Until recently, the platform was only available in English. This study describes the development of an ad hoc methodology that addresses key linguistic challenges, including domain-specific vocabulary, acronyms, word order, passive voice, syntagms, and adaptation of technical terms. The translation process focuses on ensuring textual uniformity, cohesion, and accuracy while minimizing errors in biomedical computational contexts. The methodology presented may serve as a framework for similar translation efforts in diverse non-English speaking countries. Author summary Paulo Eduardo de Barros Veiga, born in Ribeirão Preto, São Paulo (Brazil), holds degrees in Music, Language, and Literature, focusing on Criticism and Translation. He completed a postdoctoral fellowship (Process No. 2018/01418-2, São Paulo Research Foundation – FAPESP). He served as a collaborator and temporary professor in the Department of Music at FFCLRP, University of São Paulo, where he worked on the History and Philosophy of Art. Currently, he is involved in a research project within the Department of Computing and Mathematics at the same university, focusing on developing a biomedical imaging data platform (3D Slicer Software). Under the coordination of Prof. Sonia Pujol (Harvard University) and Prof. Luiz Murta (USP), he leads the translation of the 3D Slicer software into Brazilian Portuguese and proposes solutions and methods in Portuguese. He also engages in music, translation, research, and education projects freelance. Additionally, he is a member of the Póıesis Crítica research group under NAPI-CIPEM. Paulo holds a bachelor’s, master’s, and doctoral degree in Literary Studies from the FCLAr at UNESP (São Paulo State University). He received a CAPES scholarship during his Master’s and was awarded the Emerging Leaders in the Americas Program by the Canadian government, having studied at the University of Winnipeg. He has also lived in London. Introduction The multilingual and multicultural nature of healthcare presents challenges in the usability of medical software. Language barriers prevent comprehension and effective use. Translating and localizing interfaces improves navigation, reduces errors and promotes broader adoption, considering “the engagement of regional partners in the adaptation and contextualization of the programs”([ 1 ]). Providing software in user-preferential languages enhances trust, engagement, and respect for cultural diversity. In this context, biomedical translation can be complex due to nuanced terminology. Automated tools often miss subtleties, cultural traces, and idiomatic peculiarities, leading to misunderstandings [ 2 ]. Errors in menus or prompts can cause misuse, while mistranslated flawed conclusions [ 3 ]. AI and large language models (LLM) also struggle with domain-specific jargon in medicine [ 4 ]. Thus, although LLMs offer remarkable progress in automated translation, their application in critical domains such as medicine requires deliberate methodological frameworks that include intense human intervention, through safe translation practices. This approach does not diminish the value of AI - useful for translation tasks and large volumes of texts - but rather ensures that its capabilities are applied judiciously, promoting linguistic responsibility, a key concept in this approach. The use of a foreign language in medical software increases cognitive load, affecting comprehension and decision-making [ 5 ] [ 6 ]. The Foreign Language Effect shows that second-language use leads to more analytical but less emotional decisions, due to distinct neural processing [ 7 ]. Neuroimaging confirms that foreign language processing activates different brain areas [ 8 ], impacting tasks like reading, visual search and interaction with complex terminology. Language also shapes health-related decisions, such as care-seeking behavior among medical students, influenced by linguistic and grammatical structures. Understanding these effects supports better design for nonnative English users. Concerning this linguistic-computational scenario, this article presents the translation process of the software 3D Slicer into Portuguese, initiated in July 2023, with focus on an ad hoc methodology designed to ensure textual uniformity, cohesion, and accuracy. By reducing errors and expediting revision, the method offers a systematic approach to software translation, contributing a novel procedural framework. The 3D Slicer is a free, open-source and comprehensive software for analyzing 3D medical image datasets, among other processes, including being as a hub of a diverse global community. It is intended for use by researchers, the medical community, and developers, facilitating efficient improvement and distribution of new methods to clinical users. However, 3D Slicer is not yet authorized for clinical purposes, only for research. The software allows for easy package installation with its Python and C++ extensibility and serves as a Jupyter kernel with remote 3D rendering capabilities. The 3D Slicer enables analysis, segmentation, and visualization of medical images, demonstrating versatility for various types of organ data [ 9 ] [ 10 ]. Despite its versatility, language barriers may hinder its accessibility, limiting its adoption among non-English-speaking researchers and healthcare professionals. As part of the 3D Slicer project, the “3D Slicer for Latin America: Localization and Outreach” [ 3 ] aims to enable biomedical researchers in Latin America to use the 3D Slicer platform. It involves adapting the software to Spanish and Portuguese in America and improving interoperability with local and cultural information systems. In addition, it proposes a more effective integration with region-specific databases to enrich biomedical research capabilities. In this context, translating medical software like 3D Slicer poses linguistic challenges due to its technical nature and multilingual context. Key issues include domain-specific vocabulary, acronyms, word order, passive voice, syntagms and the adaptation of foreign terms. Each demands targeted solutions to preserve clarity, precision, and cultural sensitivity, especially given that 3D Slicer is not merely a tool, but a multidisciplinary, intercultural community. Domain-specific terms must avoid ambiguity; acronyms require contextual adaptation; and structural shifts - such as adjusting passive constructions and syntagms - ensure fluency in Portuguese. Balancing accuracy and cultural-linguistic coherence is essential when integrating foreign terminology. Addressing these demands involved both linguistic and technical expertise, supported by collaboration with experienced colleagues and specialized tools. These elements were key to developing the ad hoc methodology presented here. The following sections will outline the materials and methods used to establish this methodology, detailing the linguistic resources employed, the solutions developed, and the structured approach taken to ensure translation accuracy. Materials and methods A specific methodology was developed to translate the 3D Slicer into Portuguese. This methodology represents an ad hoc process characterized by its personalized approach and particular focus on the context of the “3D Slicer and the Latin America localization and outreach project.” The proposed linguistic topics may vary depending on the translation context and the nature of the software. Therefore, it is not an a priori methodology but a method built as one engages with the linguistic object, formulating an innovative process. The methodology begins by gathering 300 English sentence samples from 3D Slicer for translation into Portuguese out of 5382 sentences. At least in the initial stages, this excludes the Slicer CTK, Language Packs, MONAILabel, Slicer VMTK, and IGT, encompassing subsequent translation steps. The subset of 300 sentences (5.57% of the 3D Slicer) was then used for preliminary evaluation to identify significant linguistic challenges. Although not all modules were included due to feasibility constraints, the methodology remains applicable and reproducible across all components, covering the entire translated software. A solution and model examples were devised for each linguistic topic to guide similar cases. Thus, the translation processes and the establishment of the methodology involve using translation tools, such as dictionaries, grammar, academic databases, and pertinent literature in translation and technology contexts. This process established guidelines for the translation effort, expediting and minimizing errors during the revision stage, and was completed in less time than expected. [ 11 ] One key component in ensuring the success of any medical software, including 3D Slicer, is the implementation of a comprehensive glossary. A glossary is vital to any medical software system [ 12 ]. It promotes standardized communication, improves user experience, enhances training, ensures data accuracy, facilitates compliance, enables interoperability, and future-proofs the software. Medical terminology is complex and filled with jargon. A glossary ensures that all software users, from healthcare professionals to administrators, have a shared understanding of the terms used within the system. This helps prevent misinterpretations and errors that could negatively impact patient care. Glossaries also make the software more user-friendly, especially for those less familiar with medical terminology. Users can navigate the system more confidently and efficiently by providing clear definitions and explanations. A glossary can be a valuable training tool for new users. It provides a quick reference for unfamiliar terms, reducing the learning curve and speeding up the onboarding process. A glossary ensures that medical data is entered and interpreted consistently across the system. This improves data accuracy and facilitates effective data analysis and reporting. In many countries, healthcare software is subject to strict data management and privacy regulations. A glossary can help demonstrate compliance by ensuring that medical terminology is used accurately and consistently. As healthcare systems become increasingly interconnected, the ability to exchange data seamlessly is vital. A standardized glossary of terms can facilitate interoperability between different software systems, enabling the smooth flow of information. Medical terminology and practices evolve. A glossary can be easily updated to reflect these changes, ensuring the software remains relevant and effective in the long term. The glossary translation was part of the software translation process, encompassing the methodological process presented in this article. Regarding the Portuguese language, the methodology identified six key linguistic topics relevant to software translation and outlined primary challenges based on the sampled data. As detailed in Section V, a flowchart was developed for each topic, constituting the methodology’s central component. The diagrams were designed to enhance the visualization of the translation process by applying basic principles of algorithmization to systematize the sequence of stages. The process begins with an example-problem or problem-sentence, representing the challenge the translator must tackle. This methodology was developed to solve textual problems while standardizing the quality of translations in a biomedical background. From the problem-sentence, a translation hypothesis is formulated—either more literal or contextually driven—enabling a flexible translation approach. This hypothesis is then tested against the broader context, incorporating various references, called the world-context. Every translation hypothesis must engage in a dialogue with a wider world of texts, where the accuracy of the translation can be evaluated. The diagrams guide the process by questioning the occurrence of similar translations within the relevant field, thus arriving at an occurrence-based solution for the problem-sentence. This approach aims to ensure high-quality and linguistically secure translations, particularly in the sensitive domain of biomedical texts. [ 13 ] The sampling effectively identified key translation issues and facilitated the formulation of solutions to optimize the process. These topics encompass domain vocabulary, acronyms, word order, passive voice, syntagms, and Portuguese linguistic adaptation. Other issues, such as verbal omission, could be included. However, because of their linguistic obviousness, no processes or patterns are developed for these topics, even though they are part of the translation task. One may add that in software translation, it is important to consider not only the verbal elements but also the visual layout of the 3D Slicer interface. Nonverbal aspects often play a key role in shaping meaning and guiding user interaction, binding the pragmatic and semantic levels. After all, meaning is also influenced by the spatial arrangement and interactive design in which language and interactions appear. Linguistic Tools The comprehensive analysis of 300 translated sentences aimed to identify patterns between various linguistic elements, including specific domain terminology, acronyms, sentence structure, verb forms, adaptation of foreign words, syntactic nuances, and phrase organization. Linguistic tools have facilitated the standardization process and solved linguistic issues. Therefore, it is frequently included in flowcharts. Throughout the translation process, cross-referencing of the text ensured lexical accuracy and accommodated newly introduced terms or neologisms. The “Vocabulário Ortogŕafico da Ĺıngua Portuguesa” (VOLP) [ 14 ] was used as the authoritative lexical reference for Portuguese. In addition, dictionaries such as the Houaiss [ 4 ] and Oxford Dictionary [ 5 ] were constantly consulted. To validate translations, an extensive search was conducted in academic repositories [ 6 ] [ 7 ] [ 8 ], among others, reviewing articles and theses from Brazilian and international universities, focusing on academic texts in the relevant field. This methodology ensured linguistic precision and facilitated comparative analyses with other languages, notably Spanish and French. Additionally, Slicer Strings Finder [ 9 ] was instrumental in identifying specific phrases within the project context. This tool allowed for the location and comparison of sentences within the software environment, providing a valuable means of verifying translations. In pursuit of improved accuracy, this contextual tool proved essential for achieving semantic precision and maintaining alignment with the software’s visual design. These materials form the essential linguistic tools. They are used when the flow chart refers to ‘linguistic tools’. Results and Flowcharts To address the challenges of translating open software such as 3D Slicer, a structured methodology was developed, applying principles of algorithmization to optimize the translation process, as illustrated in the figures presented in this paper (flowcharts). This methodology frames each linguistic issue as a “problem-sentence”, formulates translation hypotheses, and iteratively tests these hypotheses within broader contexts. Integrating linguistic tools, algorithmic approaches, and collaborative efforts – bringing together Mexican and Brazilian teams in an international setting – ensures accurate and contextually appropriate translations. Beyond improving quality and consistency, this methodology enhances usability, facilitates adoption, and mitigates risks associated with linguistic errors in biomedical applications. Despite structural and cultural differences between Portuguese and Spanish, the ability of Portuguese-speaking team members to understand written and spoken Spanish significantly facilitated communication and comprehension. This linguistic affinity not only streamlined the exchange of information but also made possible a sense of cultural cohesion within the Latin American teams. In this collaborative environment, the didactic contributions of the Mexican team proved particularly valuable, enabling the Brazilian team to navigate challenges more effectively. As a result, this integrative translation approach strengthened cross-cultural connections within the 3D Slicer environment, ultimately enhancing the linguistic experience for users. Specifically, this section outlines the established patterns for each linguistic topic identified through the ad hoc methodology. It presents the linguistic topics discerned from the sampling process with synthetic textual explanations and schematic figures without aiming to create an exhaustive grammar list. The topics are domain or field vocabulary, acronyms, word order, passive voice, syntagms, and Portuguese linguistic adaptation. Each flow chart describes the translation processes. This was the most concise way to present the methodology. Domain or Field Vocabulary Domain vocabulary refers to specialized and unique terms for specific fields or professions. Translators, professionals, and scholars in specialized areas, particularly concerning the 3D Slicer and its array of biomedical-technological terms, must employ the appropriate vocabulary accurately. Misapplication of these terms can lead to misunderstandings and compromise the integrity of the translated content. Considering this vocabulary issue, the scheme depicted in Figure 1 focuses primarily on ensuring domain-specific translation within a particular area of expertise. This approach guarantees the precise treatment of technical and specialized vocabulary during translation, thus preventing terminological distortions and upholding conceptual fidelity, nuances, and standards specific to the field. This is an essential pattern when translating software. Download figure Open in new tab Fig 1. Pattern – Domain vocabulary. The diagram presents a process for translating technical terms. Starting with the problem-sentence, a freer translation is proposed and verified in academic sources. If found, it’s confirmed; otherwise, linguistic tools aid in revision. This ensures the translation is contextually accurate. Examples from 3D Slicer demonstrate this process, showing how literal translation often aligns with field-specific terminology. The diagram in Fig. 1 outlines a translation process that begins with the problem-sentence the linguist will face: “Otsu Percentile Threshold”, a technical term. The first step is to propose a linguistically more flexible translation, forming the basis for the hypothesis: “Patamar Otsu de Percentil” in Portuguese. This hypothesis is then verified in academic sources to determine its presence in the broader world context, as the translation must be anchored within its field of discourse, as a world text or a text-in-the-world. If the term is found ipsis litteris , it is confirmed, and the process ends. Linguistic tools are consulted if the term is not or partially found, resulting in a revised translation. So, “Limiar de percentil de Otsu” backed by references to academic texts that explore the concept and Otsu’s threshold algorithm. As for additional examples, 3D Slicer contains numerous technical terms from the biomedical field, creating an exhaustive list that would be impractical to detail. In most cases, the literal or direct translation aligns with the terminology commonly used in the field. For instance, “Feret diameter in mm” was translated as “Diâmetro de Feret em milímetros”. In such cases, the translator had to cross-reference the term within its specific context, using the step-by-step diagram as a methodological guide. Acronyms Translating acronyms poses a challenge and holds significant importance in ensuring accurate comprehension in translations. [ 15 ] While more common in English, using acronyms is less prevalent or even avoided in Portuguese. Acronyms represent linguistic constructs specific to a particular language and culture, often deeply embedded in social and professional contexts. Merely substituting them in the target language, altering only the order of letters, can result in loss of meaning or misinterpretation. Therefore, precise translation of acronyms is paramount for preserving terminological accuracy and cultural connotations, safeguarding the translation’s integrity and communicative effectiveness, notwithstanding the sentence’s length or the display of buttons in 3D Slicer. The two flowcharts in Fig. 2 explain the methodological process of translating acronyms from English to Brazilian Portuguese. The first flowchart outlines the general steps: locate the acronym, translate it literally or nearly so, verify if the acronym exists or functions in Portuguese, and then decide whether to keep it in English, use it in Portuguese, or fully translate it into a non-acronym form. The second flowchart provides a specific example drawn from the 3D Slicer context, illustrating the application of this process. It demonstrates how to address the challenges of handling acronyms in languages that culturally use them in very different ways, depending on whether they are used in Brazilian Portuguese or should remain in English due to field-specific conventions or bibliographic references. There are numerous examples of acronyms throughout 3D Slicer. The use of acronyms is indeed significant and culturally impactful for Portuguese speakers. Beyond the example described in the diagram, we also have the occurrence of “FA” in a biomedical imaging context, which stands for Fractional Anisotropy. Translating it into Portuguese as “AF” (Anisotropia Fracionada) would create unnecessary confusion since the medical field is more accustomed to “FA” in English than to “AF.” The best solution to cater to a broad 3D Slicer audience is to resolve the acronym by spelling out “Anisotropia Fracionada.” Similar cases occur with “DTI” (Diffusion Tensor Imaging), translated as “Imagem por Tensor de Difusão,” and “PET” (Positron Emission Tomography), translated as “Tomografia por Emissão de Pósitrons.” When text length reduction is needed, keeping the acronym in English is preferable. Download figure Open in new tab Fig 2. Pattern – Acronyms. The two flowcharts guide translating acronyms from English to Brazilian Portuguese. The first chart outlines general steps: locating the acronym, translating it, checking its existence/function in Portuguese, and deciding on its final form (English, Portuguese, or complete translation). The second chart illustrates this process with an example from 3D Slicer, addressing challenges in handling acronyms between languages with different conventions. It emphasizes considering field-specific usage and clarity for the target audience. Examples highlight the importance of making informed choices, like keeping “FA” in English due to medical field conventions or using complete translations for clarity. The goal is effective communication in the target language, balancing cultural nuances and field-specific needs. Word Ordering Considering word order in translation is crucial due to structural differences between languages, which can impact clarity and coherence. [ 16 ] In this context, word ordering (with ing) refers to determining the most appropriate word order during translation. English typically follows the adjective-noun order, while Portuguese favors the noun-adjective order, resulting in reversed ordering rules, as a pattern that can also be observed. Simply substituting words may not be enough to maintain the original meaning; reordering is often necessary for comprehensibility and naturalness. Sometimes, a significant structural change in the sentence may be needed. Neglecting word order can lead to awkward or incoherent translations. Adapting sentence structure according to target language conventions ensures effective communication. The Fig. 3 flowchart offers a structured approach to ensuring that translations maintain syntagmatic coherence and semantic integrity when transitioning into Portuguese. Furthermore, according to the diagram, the notation “(((Closing) (%1)) ((running) (modules)))” is demarcated with brackets to delineate syntagmatic blocks or segments, reflecting a generative structuring model (Fuhrhop, 2007) in a simplified visual representation. This segmentation reveals the following syntagmatic levels: (Closing), (%1), (running), (modules) at the base level; (Closing %1) and (running modules) at an intermediate level; and finally, (Closing %1 running modules) as the fully integrated structure. Download figure Open in new tab Fig 3. Pattern – Word Ordering. The image presents a flowchart explaining the word ordering process during Portuguese translation, emphasizing maintaining semantic clarity. The diagram is divided into two sections: the general process and an example illustrating the method. Initially, the translator must examine the syntagmatic arrangement of the original text, focusing on its syntactic structure. Following this analysis, the syntagms are reordered to fit Portuguese grammatical conventions, particularly by prioritizing noun-adjective and subject-verb-complement canonical structures. Once the syntagms are properly arranged, the translation is carried out. A decision point then follows: if semantic issues arise, the text must be reordered with priority given to meaning before translation is attempted again; if no issues are identified, the process concludes. An example demonstrates this approach, where the original text, “Closing %1 running modules” is translated into “Encerrando %1 dos módulos em execução” preserving the appropriate syntactic structure in Portuguese. The translation process is finalized once a semantic check verifies that there are no further issues. Another example is the original sentence: “Confirm source representation change.” The best approach is to preserve the syntagmatic ordering of Portuguese during translation, yielding: “Confirmar a alteração da representação da fonte.” In Portuguese, adjectives often need to be transformed into adjectival phrases, using a preposition followed by a noun, to make the sentence more natural to native speakers. All methods presuppose such adjustments to ensure fluency and a natural flow for the native ear. Translation is always an exercise in good judgment, going beyond rigid rules. Generally, in terms of sentence structure, Portuguese tends to adopt the reverse order of English, with the syntagmatic order being translated inversely. For instance, “harden regularization transform” is most naturally translated as “transformação de regularização robusta.” In this case, the first word in English becomes the last in Portuguese, and so forth. Additionally, it is crucial to follow nominal regency, which often requires the inclusion of a preposition and, when necessary, a definite article in Portuguese. However, this is not always the case. Some sentences maintain the same order in translation, such as “Make source” which is translated as “Gerar fonte” where the first word in English corresponds directly to the first word in the Portuguese translation, as well as the last one. There are also hybrid sentences where part of the syntagmatic sequence is preserved, including the first word. In contrast, another part is reordered, as in “Update %1 representation using custom conversion parameters” translated as “Atualizar a representação %1 utilizando parâmetros de conversão personalizados”. The issue of sentence ordering must be considered in all translated sentences, except for those involving single words or untranslated elements such as command lines (”SlicerCapture.avi”; “SlicerCaptureLightbox.png”; “H.264” etc.). [ 17 ] 0.1 Passive Voice The utilization of passive voice in English and Portuguese exhibits variances primarily in their respective frequencies of use rather than in structural disparities. In both languages, the passive voice is constructed using the form of the verb “to be” followed by the past participle of the main verb. However, while the passive voice finds greater prevalence in English, particularly within formal contexts, Portuguese tends to favor the active voice to express similar notions. [ 18 ] Furthermore, Portuguese grammar allows one to use the “synthetic passive voice,” which is more prevalent and notably more concise in such contexts. Consequently, linguistic usage emerges as a determinant in this context, warranting careful consideration according to the mother tongue, thus underscoring its significance as an essential linguistic pattern. The flowchart in Fig 4 outlines a decision-making process for translating English sentences in the passive voice into Portuguese. Initially, the flowchart branches depending on whether the English sentence is identified as passive. If the sentence is in the passive voice, the translation begins with an assessment to determine if a synthetic passive construction is more appropriate in Portuguese. Given the grammatical flexibility of Portuguese in this topic, both synthetic (using the pronoun “se”) and analytic passive constructions are valid options. If the synthetic passive is suitable, the sentence is adjusted accordingly; otherwise, the analytic passive is maintained. On the other hand, if no passive voice is detected in the English sentence, a direct translation in the active voice is performed. Download figure Open in new tab Fig 4. Pattern – Passive Voice. The flowchart presents a process for translating English passive sentences into Portuguese. It starts by identifying if the English sentence is passive. If it is, the translator chooses between a synthetic passive (using the pronoun “se”) or an analytic passive based on suitability. If not passive, a direct active translation is used. This highlights the flexibility of Portuguese passive constructions, challenging the idea that English is more synthetic. Examples demonstrate how to apply the process and emphasize the importance of verb agreement in Portuguese. For example, the English sentence “The DataProbe module is used to get information about the current RAS position” can be translated into Portuguese using the analytic passive and then adapted to a synthetic passive structure. This adaptation often results in a more natural and concise translation, reflecting the grammatical options in Portuguese and challenging the common belief that English is inherently more synthetic than Portuguese. The final Portuguese rendering, “Usa-se o módulo DataProbe para obter informações sobre a posição atual do RAS” exemplifies this transformation. It is worth noting that while the synthetic passive is sometimes debated as a form of active voice, this analysis adheres to the traditional normative perspective due to ongoing grammatical discussions. Another example is the sentence: “Location, size, and shape of initial segments and content of source volume are taken into account.” In this case, the synthetic passive is the most appropriate choice for a smooth and concise translation. The Portuguese equivalent, “Consideram-se a localização, o tamanho e a forma dos segmentos iniciais e o conteúdo do volume de origem” highlights the importance of verb agreement with plural subjects in Portuguese. In cases where an entirely passive meaning is required in the target language and the synthetic passive is not suitable, the analytic passive takes precedence. For instance, the sentence “This work was funded by CCO ACRU and OCAIRO grants” is translated into Portuguese as “Este trabalho foi financiado pelos subsídios de ACRU, CCO e OCAIRO” preserving the passive voice. Syntagms In generative grammar, a syntagma refers to a syntactic unit comprising one or more words that function together as a cohesive unit. It typically includes a headword or phrase that dictates the nature of the syntagma, accompanied by elements such as nouns, adjectives, verbs, adverbs, and prepositions. Analyzing syntactic units can aid in translation tasks by enhancing comprehension of sentence structure, maintaining cohesion and coherence, selecting appropriate equivalents, and facilitating cultural adaptation. The image in Fig. 5 presents a flowchart that outlines translating syntagms from English to Portuguese. It begins by analyzing the syntagmatic structure in English, with particular attention to specific concepts within the relevant field. Next, the full translation of the syntagms is attempted based on the English sentence, using linguistic tools to scan for syntagmatic patterns. If an exact match for the full syntagm exists, the translation follows the standard terminology used in the field of knowledge. However, if the full syntagm does not exist, the process involves breaking the expression into smaller syntagms, checking each one individually, and rearranging them until the correct terms are identified. Once the syntagms are located, they are reassembled, completing the translation. For example, after analyzing its syntagmatic components, the phrase “Standard deviation of scalar values” is translated as “Desvio padrão de Valores escalates.” If needed, the syntagms are separated and then correctly recombined during the process. Download figure Open in new tab Fig 5. Pattern – Syntagms. The image provides a flowchart illustrating translating phrases from English to Portuguese. It involves analyzing the syntagmatic structure in English and using linguistic tools to find syntagmatic patterns. If an exact match exists, the translation follows standard terminology. If not, the phrase is broken down, each component is checked, and rearranged until the correct terms are found. The translated components are then reassembled to complete the translation. The critical issue in the diagram in Fig. 5 , which should not be confused with sentence ordering, is finding the exact terms in the biomedical-computational literature. Extended expressions, during translation, can often become confusing in the resulting text. Breaking down the syntagms to find the correct corresponding terms is necessary, ensuring they align with the specific area of expertise. Another example is the sentence “Change source representation to binary labelmap?” where the syntagms need to be grouped to identify pairs of terms that function together and should remain connected. Notably, “source representation” (representação de origem) and “binary labelmap” (mapa de ŕotulos bińario) must be kept as grouped pairs for an accurate translation. Portuguese Adaption Adapting foreign terms to one’s native language, with a preference for the latter, is fundamental for effective communication and the preservation of linguistic identity. It is imperative to prioritize Portuguese terms as a guiding principle thoroughly. However, when these terms fall short, retaining foreign language terms becomes necessary to prevent Anglicisms and maintain linguistic integrity. This approach fosters clarity and ensures that language reflects the cultural nuances of its speakers. This proposal aligns with the “3D Slicer for Latin America: Localization and Outreach” objectives, emphasizing the importance of linguistic authenticity and cultural preservation. The image illustrates a flowchart for adapting neologisms or specialized field terms from English to Brazilian Portuguese (PT-BR). The process begins by identifying the neologism or specific term. If an established translation exists, it is used directly. If not, the term undergoes translation and adaptation into PT-BR. Next, linguistic tools are consulted to verify the correct spelling and accentuation. If the adapted word fits PT-BR standards, it is used. If it does not, the original English term is retained. For example, “Number of voxels” is first translated as “Número de vóxeis.” Since the adapted word works within PT-BR linguistic conventions, it is used to complete the process. The same principle applies to the word “pixels,” which becomes an adapted term in Portuguese through accentuation, resulting in “píxeis.” A notable case is the sentence “Set reference image geometry and resamples all segment label maps,” where the English term “resample” appears. In informal Portuguese, the neologism “reamostra” and its variants (reamostragem, reamostrar) could be used. However, the Vocabuĺario Ortogŕafico da Língua Portuguesa (VOLP), a key tool for confirming officially registered words, omits “reamostrar.” Although the rule is to primarily follow the VOLP, in this case, due to the contingent use of the term, we adopt the neological use of “reamostrar” and its variants. Possible Flowcharts While other topics, like verbal omission, could have been considered, their exclusion is justified by their inherent linguistic clarity. Although not addressed, the absence of a formal process or pattern for these topics does not detract from the overall translation task. The development of a flowchart illustrating the system of verbal omission would reveal that, for specific grammatical issues, the flowchart would be so concise that a predefined pattern would be unnecessary. In such cases, the absence of a formal pattern would ensure quality and consistency in translation. Flowcharts were only created for linguistic circumstances where standardization would enhance the rigor of the translation, ensuring uniformity and textual quality. For example, compared to the other charts, only a basic flowchart on verbal omission is provided as an example of its dispersibility. Download figure Open in new tab Fig 6. Pattern – Portuguese adaptation. The image depicts a process for adapting English neologisms or specialized terms into Brazilian Portuguese. If an established translation exists, it’s used. Otherwise, the term is translated and adapted. Linguistic tools are then consulted to ensure correctness. If the adapted word fits Portuguese standards, it’s used; if not, the original English term is kept. The process also highlights the importance of using official linguistic resources but acknowledges the need for flexibility when dealing with neologisms in specific contexts. Verbal omission, a common linguistic phenomenon in both English and Portuguese, involves the exclusion of certain verbs from sentences without compromising their grammatical integrity or clarity of meaning. In Portuguese, the omission of verbs can occur more frequently and across various sentence types, reducing sentence length and complexity. This linguistic feature enables Portuguese speakers to convey information more succinctly and efficiently, facilitating smoother communication and aiding in comprehending written and spoken discourse. Translators must consistently consider the following step: ‘Can any verbs be omitted?’ This is especially pertinent for state verbs and gerunds. Such consideration can lead to shorter sentences in Portuguese than in English without compromising clarity or fidelity to the original text. The flowchart in Fig. 7 outlines a decision-making process for determining whether verbs can be omitted in a given translation task. It begins by instructing the user to identify all verbs and gerunds in the source text. The next step presents a decision point: if the meaning of the text can be conveyed without using any verbal or verbal-nominal forms, the verbs can be omitted. However, if removing the verbs would compromise the meaning or grammatical structure of the sentence, all verbs must be retained. This flowchart offers a clear framework for translators when considering verb omission, which can be a complex aspect of translation. State verbs such as “ser” and “estar” (to be, in English) can often be omitted to avoid unnecessarily lengthening sentences, especially in software translation. Another example: “Video creation failed: ffmpeg executable path is invalid: path.” In this case, omitting the verb (is) makes the sentence more direct and concise: “Falha na criação do vídeo: caminho executável ffmpeg inválido: path.” Download figure Open in new tab Fig 7. Pattern – Portuguese adaptation. The flowchart presents a simple decision-making process for translators to determine whether or not to omit verbs in a translation. It starts with identifying all verbs and gerunds and then checking if the meaning is preserved without them. If so, they can be omitted; otherwise, all verbs must be kept. This approach is helpful, especially in software translation, to avoid overly long sentences. Furthermore, the flowchart in Fig. 7 outlines a decision-making process for determining whether verbs can be omitted in a given translation task. It begins by instructing the user to identify all verbs and gerunds in the source text. The next step presents a decision point: if the meaning of the text can be conveyed without using any verbal or verbal-nominal forms, the verbs can be omitted. However, if removing the verbs would compromise the meaning or grammatical structure of the sentence, all verbs must be retained. This flowchart offers a clear framework for translators when considering verb omission, which can be a complex aspect of translation. State verbs such as ser and estar (to be, in English) can often be omitted to avoid unnecessarily lengthening sentences, especially in software translation. Another example: “Video creation failed: ffmpeg executable path is invalid: path.” In this case, omitting the verb (is) makes the sentence more direct and concise: “Falha na criação do vídeo: caminho executável ffmpeg inválido: path.” Discussion The study successfully translated the open-source 3D medical image processing software, 3D Slicer, into Portuguese (PT-BR). It developed a specific translation methodology to address software localization’s unique challenges. This methodology provides a clear framework for translators to determine when verb omission is appropriate without compromising the sentence’s meaning or grammatical integrity. The translation process emphasized maintaining textual uniformity, cohesion, and accuracy throughout, thus minimizing errors and expediting revisions. It also tackled other linguistic challenges, such as domain-specific vocabulary, acronym usage, word order, verbal voice, and syntactic and syntagmatic arrangements. The study underscores the importance of making medical software accessible to non-English-speaking regions, particularly Latin America. It highlights the development of a specific translation methodology. It covers vital linguistic topics such as domain-specific vocabulary, acronym usage, word order, verbal voice, syntactic and syntagmatic arrangement, and linguistic adaptation. The translation process aimed to ensure textual uniformity, cohesion, and accuracy throughout the translation, thereby reducing errors and speeding up the revision step. The methodology involved gathering over 300 English sentence samples from 3D Slicer for translation into Portuguese and identifying significant linguistic challenges. Solutions and model examples were devised for each linguistic topic to guide similar cases. The investigation also involved practical and interactive elements, such as organizing outreach events and tutorials in Latin America, providing hands-on training to users, and covering both technical aspects and practical applications of 3D Slicer in medical research. By ensuring that 3D Slicer is available in Portuguese, the study highlights a significant step towards improving healthcare access and quality in these regions. The meticulous approach ensures that the translation is not only accurate but also fluid and natural for native speakers, thereby enhancing usability and effectiveness. Our study presents several distinctive improvements and novel methodologies compared to previous studies and existing literature on software translation. Traditional approaches often emphasize literal translation, resulting in stilted and unnatural text that may confuse users. In contrast, our methodology prioritizes fluidity and naturalness alongside accuracy, ensuring that the translated text resonates well with native speakers. One unique aspect of our approach is the targeted handling of domain-specific vocabulary and the strategic use of acronyms. Where other methodologies might overlook the subtleties of technical jargon, our method explicitly addresses these elements, ensuring precise and contextually accurate translations. This focus not only enhances the software’s readability for professional users but also maintains the technical integrity of the content [ 19 ] [ 20 ]. Another significant improvement is our treatment of state verbs such as “ser” and “estar.” While previous studies have either fully retained or omitted these verbs, our approach provides a balanced framework. We developed criteria for when verb omission can enhance the sentence without losing meaning or grammatical integrity. This nuanced method avoids unnecessarily lengthening sentences, especially in the highly specific context of software translation, thus making the translated interface more user-friendly. Furthermore, our methodology includes comprehensive guidelines on syntactic and syntagmatic arrangements. This aspect is often underemphasized in other studies, but we recognize its importance in ensuring that translations are accurate, coherent, and cohesive. By providing model examples and practical solutions, we equip translators with the tools to tackle complex linguistic challenges effectively. Overall, the unique aspects of our translation methodology—such as the focus on domain-specific vocabulary, acronym usage, and linguistic adaptation—represent significant advancements over existing methods. These innovations contribute to a more intuitive and practical user experience, particularly in non-English-speaking regions like Latin America, thereby broadening the accessibility and usability of medical software like 3D Slicer. Translating the 3D Slicer software into Portuguese (PT-BR) carries profound practical implications, particularly for healthcare professionals and patients in Latin America. One of the most significant benefits is the improved accessibility to advanced medical imaging tools. By offering the software in a language widely spoken in the region, healthcare providers can more easily integrate 3D Slicer into their practices, enhancing their diagnostic and treatment capabilities. Moreover, meticulous attention to linguistic details, such as domain-specific vocabulary and acronym usage, ensures that the translated software is accurate, fluid, and natural for native speakers. This reduces the cognitive load on healthcare professionals, allowing them to focus more on patient care rather than struggling with language barriers. Using a coherent and cohesive translation methodology minimizes the risk of misinterpretation and errors, which is crucial in a medical context where precision is paramount. The translated 3D Slicer software means better and more accurate patient diagnoses, as healthcare providers can utilize the software more effectively. This can lead to improved treatment outcomes and overall better healthcare experiences. Additionally, the availability of advanced medical imaging tools in their native language empowers patients, making them more likely to engage in their healthcare processes and understand their medical conditions better. Another critical implication is the potential for increased adoption of the 3D Slicer software in Latin America. As more healthcare facilities and professionals become aware of and comfortable using the software in Portuguese, its adoption rates are likely to rise. This widespread use can lead to a more standardized approach to medical imaging in the region, fostering collaboration and knowledge sharing among medical professionals. In conclusion, the translated 3D Slicer software can potentially transform healthcare delivery in Latin America. By enhancing accessibility, reducing the risk of errors, and encouraging the adoption of advanced medical imaging tools, the translation project significantly improves healthcare quality and outcomes for professionals and patients in the region. Conclusion Translating the 3D Slicer software into Portuguese for Latin America has demonstrated significant advancements in making medical open-source software more accessible to non-English speaking regions. This project has addressed the linguistic barriers and highlighted the importance of cultural adaptation in software localization. The ad hoc methodology developed for this translation process has proven effective in ensuring textual uniformity, cohesion, and accuracy, thereby reducing errors and expediting the revision stage. The collaboration between Brazilian, Mexican, and American teams has been instrumental in overcoming initial challenges and fostering a sense of cultural cohesion among Latin American teams. The integration of linguistic tools and the development of a comprehensive glossary have further enhanced the quality and consistency of the translations. This project underscores the importance of international collaboration and the need for tailored linguistic approaches in translating medical software. The translation process required attention to linguistic structural standards to ensure precision and coherence in the target language, particularly in translating English to Portuguese using an ad hoc methodology. This involved analyzing syntactic blocks and linguistic nuances, identifying patterns, collaborating to bridge cultural gaps, and facilitating cross-cultural communication in software translation. Translating 300 sample sentences from 3D Slicer was an initial step in establishing core standards, optimizing efficiency, ensuring uniformity, and minimizing errors and delays in the review process. This endeavor was supported by identifying and implementing translation patterns in six key linguistic topics: domain vocabulary, acronyms, word order, passive voice, syntagms, and Portuguese linguistic adaptation. These topics lay the groundwork for localization efforts, underscoring the importance of linguistic precision and cultural adaptation in biomedical and technological software translation. Furthermore, the completion of the translation process, alongside the localization efforts of the 3D Slicer, highlights the necessity for tailored solutions to linguistic challenges on a case-by-case basis. This involves using virtual translators and leveraging various sources, including academic repositories, to construct linguistic data and contextualized semantic excerpts, considering the specific field. International collaboration and systematic methodologies aim to enhance the accessibility and usability of essential medical tools such as the 3D Slicer, ultimately advancing medical research and healthcare delivery worldwide. In conclusion, successfully translating the 3D Slicer software into Portuguese sets a precedent for future localization projects. It demonstrates that it can overcome linguistic and cultural barriers with a suitable methodology and collaborative efforts, making advanced medical software accessible to a broader audience. This project not only contributes to the field of software translation but also paves the way for more inclusive and effective medical research and practice in Latin America. Data Availability All data produced are available online at www.slicer.org Acknowledgments This article was partly made possible by grants number 2021-237549 and 2022-252572 from the Chan Zuckerberg Initiative DAF, an advised Silicon Valley Community Foundation fund. We also sincerely thank Elena Barbosa Nascimento for her valuable assistance in reviewing the text and engaging in insightful linguistic discussions related to English translation and academic writing. Footnotes † Deceased ¶ Membership list can be found in the Acknowledgments section. References 1. ↵ Bélizaire MRD , Ineza L , Fall IS , Ondo M , Boum Y II . From barrier to enabler: Transforming language for global health collaboration . PLOS Global Public Health . 2024 ; 4 : 1 – 5 . doi: 10.1371/journal.pgph.0003237 . OpenUrl CrossRef 2. ↵ Wang L . The Impacts and Challenges of Artificial Intelligence Translation Tool on Translation Professionals . SHS Web of Conferences . 2023 ; 163 : 02021 . doi: 10.1051/shsconf/202316302021 . OpenUrl CrossRef 3. ↵ Akinci D’Antonoli T , Stanzione A , Bluethgen C , Vernuccio F , Ugga L , Klontzas ME , et al. Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions . Diagnostic and Interventional Radiology . 2024 ; 30 ( 2 ): 80 – 90 . doi: 10.4274/dir.2023.232417 . OpenUrl CrossRef PubMed 4. ↵ Gupta A , Rastogi A , Malhotra H , Rangarajan K . Comparative Evaluation of Large Language Models for Translating Radiology Reports into Hindi . Indian Journal of Radiology and Imaging . 2024 ; 35 ( 01 ): 088 – 096 . doi: 10.1055/s-0044-1789618 . OpenUrl CrossRef 5. ↵ Alkhammash R , Asiri YA , Alqarni IR , Al-Hoorie AH . Can Language Influence Health Decisions? The Role of Foreign Language and Grammatical Structure . Journal of Psycholinguistic Research . 2022 ; 52 ( 3 ): 957 – 974 . doi: 10.1007/s10936-022-09918-z . OpenUrl CrossRef PubMed 6. ↵ Hayakawa S , Pan Y , Marian V . Considering Preventative Care in a Native vs. Non-native Language: A Foreign Language Effect . Brain Sciences . 2021 ; 11 ( 10 ): 1309 . doi: 10.3390/brainsci11101309 . OpenUrl CrossRef PubMed 7. ↵ Perani D , Abutalebi J . The neural basis of first and second language processing . Current Opinion in Neurobiology . 2005 ; 15 ( 2 ): 202 – 206 . doi: 10.1016/j.conb.2005.03.007 . OpenUrl CrossRef PubMed Web of Science 8. ↵ Binder JR , Frost JA , Hammeke TA , Cox RW , Rao SM , Prieto T . Human Brain Language Areas Identified by Functional Magnetic Resonance Imaging . The Journal of Neuroscience . 1997 ; 17 ( 1 ): 353 – 362 . doi: 10.1523/jneurosci.17-01-00353.1997 . OpenUrl Abstract / FREE Full Text 9. ↵ Kikinis R , Pieper SD , Vosburgh KG. 3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support . In: Intraoperative Imaging and Image-Guided Therapy . Springer New York ; 2013 . p. 277 – 289 . Available from : doi: 10.1007/978-1-4614-7657-3_19 . OpenUrl CrossRef 10. ↵ Fedorov A , Beichel R , Kalpathy-Cramer J , Finet J , Fillion-Robin JC , Pujol S , et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network . Magnetic Resonance Imaging . 2012 ; 30 ( 9 ): 1323 – 1341 . doi: 10.1016/j.mri.2012.05.001 . OpenUrl CrossRef PubMed Web of Science 11. ↵ Griffiths D , Heppell S , Millwood R , Mladenova G . Translating software: What it means and what it costs for small cultures and large cultures . Computers & Education . 1994 ; 22 ( 1–2 ): 9 – 17 . doi: 10.1016/0360-1315(94)90068-x . OpenUrl CrossRef 12. ↵ Buschhardt T , Günther T , Skjerdal T , Torpdahl M , Gethmann J , Filippitzi ME , et al. A one health glossary to support communication and information exchange between the human health, animal health and food safety sectors . One Health . 2021 ; 13 : 100263 . doi: 10.1016/j.onehlt.2021.100263 . OpenUrl CrossRef 13. ↵ Karwacka W. Medical Translation . In: Bogucki L -, Gózdz-Roszkowski S , Stalmaszczyk P , editors. Ways to Translation . Wydawnictwo Uniwersytetu L-ódzkiego ; 2015 . p. 271 – 298 . 14. ↵ de Letras AB. Orthographic Vocabulary of the Portuguese Language ; 2024 . Available from: https://www.academia.org.br/nossa-lingua/busca-no-vocabulario . 15. ↵ Wren JD . Biomedical term mapping databases . Nucleic Acids Research . 2004 ; 33 (Database issue): D289 – D293 . doi: 10.1093/nar/gki137 . OpenUrl CrossRef 16. ↵ Dinh D , Tamine L. Biomedical concept extraction based on combining the content-based and word order similarities . In: Proceedings of the 2011 ACM Symposium on Applied Computing . SAC’11. ACM; 2011 . p. 1159 – 1163 . Available from : doi: 10.1145/1982185.1982438 . OpenUrl CrossRef 17. ↵ Dittmann J . Nanna Fuhrhop . 2007 . Zwischen Wort und Syntagma. Zur grammatischen Fundierung der Getrennt- und Zusammenschreibung (Linguistische Arbeiten, 513). Tübingen: Max Niemeyer . 198 S. Zeitschrift für Rezensionen zur germanistischen Sprachwissenschaft . 2010; 2 ( 2 ). doi: 10.1515/zrs.2010.041 . OpenUrl CrossRef 18. ↵ Azevedo MM . Passive Sentences in English and Portuguese . Washington : Georgetown University Press ; 1973 . 19. ↵ Mehandru N , Robertson S , Salehi N . Reliable and Safe Use of Machine Translation in Medical Settings . In: 2022 ACM Conference on Fairness, Accountability, and Transparency . FAccT ‘22. ACM; 2022 . p. 2016 – 2025 . Available from : doi: 10.1145/3531146.3533244 . OpenUrl CrossRef 20. ↵ Boulanger AM . The use of machine translation and AI in medical translation: Pros and cons . Medical Writing . 2024 ; 33 ( 1 ): 62 – 65 . doi: 10.56012/fcbh4324 . OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted September 15, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Translating 3D Slicer to Latin America Language: a Method for Making a Medical Open-Source Software Widely Available Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Translating 3D Slicer to Latin America Language: a Method for Making a Medical Open-Source Software Widely Available Paulo Eduardo de Barros Veiga , Luiz Otavio Murta Junior , Douglas Samuel Gonçalves , Lucas Sanchez Silva , Víctor M. Montaño-Serrano , Enrique Hernandez-Laredo , Valeria Gómez-Valdes , Monserrat Ríos-Hernández , Andras Lasso , Steve Pieper , Adriana Vilchis-González , Sonia Pujol medRxiv 2025.09.15.25335771; doi: https://doi.org/10.1101/2025.09.15.25335771 Share This Article: Copy Citation Tools Translating 3D Slicer to Latin America Language: a Method for Making a Medical Open-Source Software Widely Available Paulo Eduardo de Barros Veiga , Luiz Otavio Murta Junior , Douglas Samuel Gonçalves , Lucas Sanchez Silva , Víctor M. 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