BSANet: High-Performance 3D Medical Image Segmentation

Abstract

As a challenge in the field of smart medicine, medical picture segmentation gives important decisions and is the basis for future diagnosis by doctors. In the past decade, FCN-based network topologies have made amazing progress in the field. However, the limited perceptual capacity of convolutional kernels in FCN network topologies limits the network’s ability to acquire a global field of view. We propose BSANet, a 3D medical image segmentation network based on self-focus and multi-scale information fusion with a high-performance feature extraction module. BSANet can help the network to extract deeper features by obtaining a larger range of perceptual capabilities by using its self-focus and multi-scale information aggregation pooling modules. Brain tumor segmentation dataset and multi-organ segmentation dataset are used to train and evaluate our model. BSANet produces excellent results with its high-performance feature extraction network with an attention module and multi-scale information fusion module.

Keywords

• Deep learning
• FCN
• medical image segmentation