  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.
