  The accurate segmentation of Coronary Computed Tomography Angiography (CCTA)
images holds substantial clinical value for the early detection and treatment
of Coronary Heart Disease (CHD). The Transformer, utilizing a self-attention
mechanism, has demonstrated commendable performance in the realm of medical
image processing. However, challenges persist in coronary segmentation tasks
due to (1) the damage to target local structures caused by fixed-size image
patch embedding, and (2) the critical role of both global and local features in
medical image segmentation tasks.To address these challenges, we propose a deep
learning framework, TransCC, that effectively amalgamates the Transformer and
convolutional neural networks for CCTA segmentation. Firstly, we introduce a
Feature Interaction Extraction (FIE) module designed to capture the
characteristics of image patches, thereby circumventing the loss of semantic
information inherent in the original method. Secondly, we devise a Multilayer
Enhanced Perceptron (MEP) to augment attention to local information within
spatial dimensions, serving as a complement to the self-attention mechanism.
Experimental results indicate that TransCC outperforms existing methods in
segmentation performance, boasting an average Dice coefficient of 0.730 and an
average Intersection over Union (IoU) of 0.582. These results underscore the
effectiveness of TransCC in CCTA image segmentation.
