Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images.

ABSTRACT: Background: Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g., same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. Methods: We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-sc

SUBMITTER: Chen C 

PROVIDER: S-EPMC7344224 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature