Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation.

ABSTRACT:

Purpose

Accurate segmentation of liver and liver tumors is critical for radiotherapy. Liver tumor segmentation, however, remains a difficult and relevant problem in the field of medical image processing because of the various factors like complex and variable location, size, and shape of liver tumors, low contrast between tumors and normal tissues, and blurred or difficult-to-define lesion boundaries. In this paper, we proposed a neural network (S-Net) that can incorporate attention mechanisms to end-to-end segmentation of liver tumors from CT images.

Methods

First, this study adopted a classical coding-decoding structure to realize end-to-end segmentation. Next, we introduced an attention mechanism between the contraction path and the expansion path so that the network coul

SUBMITTER: Luan S 

PROVIDER: S-EPMC8381250 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature