RC-Net: Regression Correction for End-To-End Chromosome Instance Segmentation.

ABSTRACT: Precise segmentation of chromosome in the real image achieved by a microscope is significant for karyotype analysis. The segmentation of image is usually achieved by a pixel-level classification task, which considers different instances as different classes. Many instance segmentation methods predict the Intersection over Union (IoU) through the head branch to correct the classification confidence. Their effectiveness is based on the correlation between branch tasks. However, none of these methods consider the correlation between input and output in branch tasks. Herein, we propose a chromosome instance segmentation network based on regression correction. First, we adopt two head branches to predict two confidences that are more related to localization accuracy and segmentation accuracy to

SUBMITTER: Liu H 

PROVIDER: S-EPMC9158129 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature