Deep 3D Vessel Segmentation based on Cross Transformer Network

TL;DR

This paper introduces a new cross transformer network (CTN) that enhances 3D vessel segmentation accuracy by capturing global dependencies, supported by large annotated datasets for coronary microvascular disease analysis.

Contribution

The paper presents a novel hybrid model combining U-Net with a transformer module and provides large-scale annotated datasets for vessel segmentation.

Findings

CTN improves segmentation connectivity and accuracy.

Large annotated datasets support better algorithm development.

Hybrid model leverages global and local features effectively.

Abstract

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