Liver segmentation in CT imaging with enhanced mask region-based convolutional neural networks.

ABSTRACT:

Background

Liver segmentation in computed tomography (CT) imaging has been widely investigated as a crucial step for analyzing liver characteristics and diagnosing liver diseases. However, obtaining satisfactory liver segmentation performance is highly challenging because of the poor contrast between the liver and its surrounding organs and tissues, the high levels of CT image noise, and the wide variability in liver shapes among patients.

Methods

To overcome these challenges, we propose a novel method for liver segmentation in CT image sequences. This method uses an enhanced mask region-based convolutional neural network (Mask R-CNN) with graph-cut segmentation. Specifically, the k-nearest neighbor (k-NN) algorithm is employed to cluster the target liver pixel

SUBMITTER: Chen X 

PROVIDER: S-EPMC8756208 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature