Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain.

ABSTRACT: In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of stained tissue relies on machine learning driven segmentation. However, such methods require multiple additional information, or features, which are increasing the quantity of data to process. As a result, the quantity of features to deal with represents a drawback to process large series or massive histological images rapidly in a robust manner. Existing feature selection methods can reduce the amount of required information but the selected subsets lack reproducibility. We propose a novel methodology operating on high performance computing (HPC) infrastructures and aiming at finding small and stable sets of features for fast and robust seg

SUBMITTER: Bouvier C 

PROVIDER: S-EPMC8626511 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature