Heatmap-Seeded Spatially Grounded Query Transformers Improve Astrocyte Soma Detection across Immunostains and Scan Resolutions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Heatmap-Seeded Spatially Grounded Query Transformers Improve Astrocyte Soma Detection across Immunostains and Scan Resolutions Jiayang Zhu, Xizhe Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8429911/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Automated astrocyte quantification in human immunohistochemistry (IHC) is hindered by stain variability and dense clustering. Standard Transformers often miss faint somata due to agnostic initialization, while CNNs struggle with overlapping processes. Methods: We propose a Heatmap-Seeded Query Transformer coupling candidate sufficiency with global competitive assignment. Queries initialized from CNN-derived spatial peaks capture faint targets and are refined via a cross-scale Transformer decoder. We evaluated the model on ALDH1L1- and GFAP-stained human tissue (0.35–0.50 μm/px) against Faster R-CNN, YOLOv11, and DETR using COCO metrics and bootstrap-resampled FROC analysis. Results: Our method consistently improved AP across all cohorts, notably by ∼20% in highresolution ALDH1L1 images relative to baselines. FROC analysis confirmed superior sensitivity at low false-positive rates, validating effective noise suppression with high recall. Conclusions: Integrating spatial priors with global reasoning mitigates the sensitivity-precision trade-off, offering a robust tool for clinical neuropathology resilient to domain shifts in archival tissue. Astrocyte detection CNN Transformer Object detection ALDH1L1 GFAP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Mar, 2026 Reviews received at journal 01 Feb, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers invited by journal 13 Jan, 2026 Editor invited by journal 25 Dec, 2025 Editor assigned by journal 24 Dec, 2025 Submission checks completed at journal 24 Dec, 2025 First submitted to journal 22 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8429911","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575081992,"identity":"832459f2-a0ce-4722-a33c-4f44124686ad","order_by":0,"name":"Jiayang Zhu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiayang","middleName":"","lastName":"Zhu","suffix":""},{"id":575081993,"identity":"c4b59880-14bd-4b73-9d43-2d8395c85bf8","order_by":1,"name":"Xizhe 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[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Astrocyte detection, CNN, Transformer, Object detection, ALDH1L1, GFAP","lastPublishedDoi":"10.21203/rs.3.rs-8429911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8429911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Automated astrocyte quantification in human immunohistochemistry (IHC) is hindered by stain variability and dense clustering. Standard Transformers often miss faint somata due to agnostic initialization, while CNNs struggle with overlapping processes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: We propose a Heatmap-Seeded Query Transformer coupling candidate sufficiency with global competitive assignment. Queries initialized from CNN-derived spatial peaks capture faint targets and are refined via a cross-scale Transformer decoder. We evaluated the model on ALDH1L1- and GFAP-stained human tissue (0.35–0.50 μm/px) against Faster R-CNN, YOLOv11, and DETR using COCO metrics and bootstrap-resampled FROC analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Our method consistently improved AP across all cohorts, notably by ∼20% in highresolution ALDH1L1 images relative to baselines. FROC analysis confirmed superior sensitivity at low false-positive rates, validating effective noise suppression with high recall.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: Integrating spatial priors with global reasoning mitigates the sensitivity-precision trade-off, offering a robust tool for clinical neuropathology resilient to domain shifts in archival tissue.\u003c/p\u003e","manuscriptTitle":"Heatmap-Seeded Spatially Grounded Query Transformers Improve Astrocyte Soma Detection across Immunostains and Scan Resolutions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 11:05:57","doi":"10.21203/rs.3.rs-8429911/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T09:47:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T01:33:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T13:30:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137921224377370904932118852790785769250","date":"2026-01-19T12:17:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12442212219007605199624174582814857294","date":"2026-01-14T07:55:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-13T11:50:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-25T06:45:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-24T07:01:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-24T06:59:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-12-23T04:07:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"
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