A sensitivity analysis of probability maps in deep-learning-based anatomical segmentation.

ABSTRACT:

Purpose

Deep-learning-based segmentation models implicitly learn to predict the presence of a structure based on its overall prominence in the training dataset. This phenomenon is observed and accounted for in deep-learning applications such as natural language processing but is often neglected in segmentation literature. The purpose of this work is to demonstrate the significance of class imbalance in deep-learning-based segmentation and recommend tuning of the neural network optimization objective.

Methods

An architecture and training procedure were chosen to represent common models in anatomical segmentation. A family of 5-block 2D U-Nets were independently trained to segment 10 structures from the Cancer Imaging Archive's Head-Neck-Radiomics-HN1 dataset. We identify the

SUBMITTER: Bice N 

PROVIDER: S-EPMC8364283 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature