Enhanced three‐dimensional U‐Net with graph‐based refining for segmentation of gastrointestinal stromal tumours

Abstract

Abstract The gastrointestinal stromal tumour (GIST) is a common mesenchymal tumour that lacks specificity of clinical manifestations. Therefore, preoperative accurate localization and accurate prediction of tumour risk are of important clinical value. At present, the diagnosis of GIST relies mainly on manual annotation of CT by professional doctors, which is inefficient and affected by subjective factors. A GIST segmentation algorithm is proposed based on a convolutional neural network to fuse multi‐scale features. The algorithm is applied to GIST segmentation with an improved 3‐D U‐Net method. Skip connections are introduced between encoders and decoders at different layers to account for the obvious differences in tumour size between different cases, which increases the path of information transmission in the network and solves the problem that U‐Net is too weak to simultaneously extract the features of different scales. In addition, due to the difficulty of tumour labelling and the correlation between small intestine segmentation and GIST segmentation, the model of small intestine segmentation is transferred to the model of GIST segmentation. Experiments show that the proposed method achieves better performance than that of the traditional U‐Net. Finally, the graph neural network is introduced to reduce the repetitive work of doctors in refining the segmentation results.

Keywords

• image segmentation
• biological organs
• feature extraction
• image fusion
• tumours
• medical image processing