CSM-Net: Automatic joint segmentation of intima-media complex and lumen in carotid artery ultrasound images

The intima-media thickness (IMT) is an effective biomarker for atherosclerosis, which is commonly measured by ultrasound technique. However, the intima-media complex (IMC) segmentation for the IMT is challenging due to confused IMC boundaries and various noises. In this paper, we propose a flexible method CSM-Net for the joint segmentation of IMC and Lumen in carotid ultrasound images. Firstly, the cascaded dilated convolutions combined with the squeeze-excitation module are introduced for exploiting more contextual features on the highestlevel layer of the encoder. Furthermore, a triple spatial attention module is utilized for emphasizing serviceable features on each decoder layer. Besides, a multi-scale weighted hybrid loss function is employed to resolve the class-imbalance issues. The experiments are conducted on a private dataset of 100 images for IMC and Lumen segmentation, as well as on two public datasets of 1600 images for IMC segmentation. For the private dataset, our method obtain the IMC Dice, Lumen Dice, Precision, Recall, and F1 score of 0.814 +/- 0.061, 0.941 +/- 0.024, 0.911 +/- 0.044, 0.916 +/- 0.039, and 0.913 +/- 0.027, respectively. For the public datasets, we obtain the IMC Dice, Precision, Recall, and F1 score of 0.885 +/- 0.067, 0.885 +/- 0.070, 0.894 +/- 0.089, and 0.885 +/- 0.067, respectively. The results demonstrate that the proposed method precedes some cutting-edge methods, and the ablation experiments show the validity of each module. The proposed method may be useful for the IMC segmentation of carotid ultrasound images in the clinic. Our code is publicly available at https://github.com/yuanyc798/US -IMC-code.

Automated summary

In this paper, the authors propose a flexible method CSM-Net for the joint segmentation of intima-media complex (IMC) and lumen in carotid ultrasound images. The method combines cascaded dilated convolutions, squeeze-excitation module, and triple spatial attention module to enhance feature extraction and improve segmentation accuracy. The proposed method achieves superior performance compared to state-of-the-art methods, demonstrating its potential utility in clinical IMC segmentation.