RA V-Net: Deep learning network for automated liver segmentation

TL;DR

RA V-Net is an advanced deep learning model based on U-Net, designed to improve automatic liver segmentation in CT images by incorporating novel modules for feature extraction, attention, and channel dependency, achieving higher accuracy.

Contribution

The paper introduces RA V-Net with three innovative modules—CofRes, AR, and CA—that enhance feature extraction, computational efficiency, and channel attention, advancing liver segmentation accuracy.

Findings

RA V-Net achieves a DSC of 0.9654, surpassing U-Net by 0.1107.

The model attains an accuracy of 0.9968 and precision of 0.9597.

Segmentation metrics show significant improvements over baseline models.

Abstract

Accurate segmentation of the liver is a prerequisite for the diagnosis of disease. Automated segmentation is an important application of computer-aided detection and diagnosis of liver disease. In recent years, automated processing of medical images has gained breakthroughs. However, the low contrast of abdominal scan CT images and the complexity of liver morphology make accurate automatic segmentation challenging. In this paper, we propose RA V-Net, which is an improved medical image automatic segmentation model based on U-Net. It has the following three main innovations. CofRes Module (Composite Original Feature Residual Module) is proposed. With more complex convolution layers and skip connections to make it obtain a higher level of image feature extraction capability and prevent gradient disappearance or explosion. AR Module (Attention Recovery Module) is proposed to reduce the computational effort of the model. In addition, the spatial features between the data pixels of the encoding and decoding modules are sensed by adjusting the channels and LSTM convolution. Finally, the image features are effectively retained. CA Module (Channel Attention Module) is introduced, which used to extract relevant channels with dependencies and strengthen them by matrix dot product, while weakening irrelevant channels without dependencies. The purpose of channel attention is achieved. The attention mechanism provided by LSTM convolution and CA Module are strong guarantees for the performance of the neural network. The accuracy of U-Net network: 0.9862, precision: 0.9118, DSC: 0.8547, JSC: 0.82. The evaluation metrics of RA V-Net, accuracy: 0.9968, precision: 0.9597, DSC: 0.9654, JSC: 0.9414. The most representative metric for the segmentation effect is DSC, which improves 0.1107 over U-Net, and JSC improves 0.1214.