Motiro: an unified non-supervised framework for statistical analysis of probe-based confocal laser endomicroscopy videos of colorectal mucosa
Sabino, A. U.; Safatle-Ribeiro, A. V.; Maluf-Filho, F.; Ramos, A. F.
Show abstract
ObjectiveTo present Motiro, an unified framework for non-supervised statistical analysis endomicroscopy videos of the colorectal mucosa. Materials and MethodsWe 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). ResultsThe segmentation process applied to normal mucosa of pre(post)- neoadjuvant patient is presented along with the corresponding statistical analysis of morphometric parameters. DiscussionOur 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 94%
- On evaluation metrics for medical applications of artificial intelligence 94%
- Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation 94%
Similar papers in this journal
Similar papers in this journal
- Towards label-free non-invasive autofluorescence multispectral imaging for melanoma diagnosis 92%
- Determination and correction of aberrations in full field OCT using phase gradient autofocus by maximizing the likelihood function 92%
- Wide-Field Stokes Polarimetric Microscopy for Second Harmonic Generation Imaging 91%
Similar papers in this journal
- TissueGrinder, a novel technology for rapid generation of patient-derived single cell suspensions from solid tumors by mechanical tissue dissociation 93%
- Automatic detection of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms 90%
- Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.