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The coronary microvascular disease poses a great threat to human health.Computer-aided analysis/diagnosis systems help physicians intervene in thedisease at early stages, where 3D vessel segmentation is a fundamental step.However, there is a lack of carefully annotated dataset to support algorithmdevelopment and evaluation. On the other hand, the commonly-used U-Netstructures often yield disconnected and inaccurate segmentation results,especially for small vessel structures. In this paper, motivated by the datascarcity, we first construct two large-scale vessel segmentation datasetsconsisting of 100 and 500 computed tomography (CT) volumes with pixel-levelannotations by experienced radiologists. To enhance the U-Net, we furtherpropose the cross transformer network (CTN) for fine-grained vesselsegmentation. In CTN, a transformer module is constructed in parallel to aU-Net to learn long-distance dependencies between different anatomical regions;and these dependencies are communicated to the U-Net at multiple stages toendow it with global awareness. Experimental results on the two in-housedatasets indicate that this hybrid model alleviates unexpected disconnectionsby considering topological information across regions. Our codes, together withthe trained models are made publicly available athttps://github.com/qibaolian/ctn.

Deep 3D Vessel Segmentation based on Cross Transformer Network