Testing Segmentation Popular Loss and Variations in Three Multiclass Medical Imaging Problems.

ABSTRACT: Image structures are segmented automatically using deep learning (DL) for analysis and processing. The three most popular base loss functions are cross entropy (crossE), intersect-over-the-union (IoU), and dice. Which should be used, is it useful to consider simple variations, such as modifying formula coefficients? How do characteristics of different image structures influence scores? Taking three different medical image segmentation problems (segmentation of organs in magnetic resonance images (MRI), liver in computer tomography images (CT) and diabetic retinopathy lesions in eye fundus images (EFI)), we quantify loss functions and variations, as well as segmentation scores of different targets. We first describe the limitations of metrics, since loss is a metric, then we describe and te

SUBMITTER: Furtado P 

PROVIDER: S-EPMC8321275 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature