Semantic Segmentation of Gastrointestinal Tract in MRI Images Based on Asymmetric Unet Network
Automatic annotation of target areas and organs is one of the key technologies for MRI-guided radiotherapy.A method for se-mantic segmentation of hollow organs such as the gastrointestinal tract in magnetic resonance images is presented.In semantic segmenta-tion tasks,the input images are often much more complex than the output images.It is assumed that the complexity of the network is positively correlated with the complexity of the input and output images.A 12-layer asymmetric UNet network is proposed with more net-work parameters allocated to the encoder,while the decoder has only one-third of the parameters of the encoder.The proposed method achieves a DSC comprehensive score of 0.856 and a Hausdorff_95 score of 3.743 in the semantic segmentation task of the stomach,co-lon,and small intestine on the UMWGI dataset.Under the same parameter conditions,the proposed method outperforms symmetric UNet and Transformer networks,indicating that the proposed method can effectively perform semantic segmentation of the gastrointestinal tract in magnetic resonance images.The boundary segmentation is also ideal,providing a feasible solution for automated annotation of the gas-trointestinal tract in magnetic resonance images.
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