Motiro: an unified non-supervised framework for statistical analysis of probe-based confocal laser endomicroscopy videos of colorectal mucosa

ABSTRACT Objective To present Motiro, an unified framework for non-supervised statistical analysis endomicroscopy videos of the colorectal mucosa. Materials and Methods We wrote an open-source Python wrapper using ImageJ software with OpenCV, Seaborn and NumPy libraries. It generates a mosaic from the video of the mucosa, evaluates morphometric properties of the crypts, their distribution, and return their statistics. Shannon entropy (and Hellinger distance) are used for quantifying variability (and comparing different mucosa). Results The segmentation process applied to normal mucosa of pre(post)- neoadjuvant patient is presented along with the corresponding statistical analysis of morphometric parameters. Discussion Our analysis provides estimation of morphometric parameters consistent with available methods, is faster, and, additionally, provides statistical characterization of the mucosa morphometry. Motiro enables the analysis of large amounts of endomicroscopy videos for building a normal rectum features dataset to help on: detection of small variability; classification of post-neoadjuvant recovery; decision about surgical intervention necessity.