Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length.

ABSTRACT: Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the est

SUBMITTER: Atzeni A 

PROVIDER: S-EPMC11605667 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature