AMO-Net: abdominal multi-organ segmentation in MRI with a extend Unet

Abstract

Abdominal organ-related diseases have become one of the main diseases that affect people’s healthy life. MRI is a clinical diagnosis method for abdominal-related diseases. Through MRI, doctors can have a more intuitive observation of the tissue lesions in the human abdomen to make more detailed observations. Accurate diagnosis, therefore, accurate image segmentation of MRI images has very important clinical value. Traditional segmentation methods are relatively inefficient for organ segmentation with large abdominal deformation, small volume and blurry tissue edges. In this paper, we propose a AMO-Net to overcome these limitations. First, we extend the single encoder-decoder architecture to 2 layers to learn richer feature representations. Second, the feature pyramid structure is introduced into the proposed network, which can effectively handle multi-scale changes, is friendly to small target object recognition, and can be associated with remote feature information. Finally, a module called Hierarchical-Split Block is introduced to improve CNN performance. We evaluate our model on the CHAOS challenge dataset, and the final experiment proves that our model achieves better segmentation performance compared with other state-of-the-art segmentation networks.