Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Fusion Encoder: Application to Liver Tumor and Vessel 3D reconstruction

• URL: http://arxiv.org/abs/2111.13299v1
• Date: Fri, 26 Nov 2021 02:48:48 GMT
• Title: Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Fusion Encoder: Application to Liver Tumor and Vessel 3D reconstruction
• Authors: Xiangyu Meng, Xudong Zhang, Gan Wang, Ying Zhang, Xin Shi, Huanhuan Dai, Zixuan Wang, and Xun Wang
• Abstract summary: We introduce a multi-scale feature context fusion network called TransFusionNet based on Transformer and SEBottleNet. Experiments show that TransFusionNet is better than the state-of-the-art method on both the public dataset LITS and 3Dircadb and our clinical dataset. We also propose an automatic 3D reconstruction algorithm based on the trained model.
• Score: 19.048952361722876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Liver cancer is one of the most common malignant diseases in the world. Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention. In the past decades, automatic CT segmentation methods based on deep learning have received widespread attention in the medical field. Many state-of-the-art segmentation algorithms appeared during this period. Yet, most of the existing segmentation methods only care about the local feature context and have a perception defect in the global relevance of medical images, which significantly affects the segmentation effect of liver tumors and blood vessels. We introduce a multi-scale feature context fusion network called TransFusionNet based on Transformer and SEBottleNet. This network can accurately detect and identify the details of the region of interest of the liver vessel, meanwhile it can improve the recognition of morphologic margins of liver tumors by exploiting the global information of CT images. Experiments show that TransFusionNet is better than the state-of-the-art method on both the public dataset LITS and 3Dircadb and our clinical dataset. Finally, we propose an automatic 3D reconstruction algorithm based on the trained model. The algorithm can complete the reconstruction quickly and accurately in 1 second.

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