MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice loss

Carotid artery stenosis risk stratification is one of the most sought-after methods for diagnosing the chances of stroke. There is an inherent requirement to identify the risk before its onset through techniques such as ultrasound imaging. The carotid artery intima-media thickness, a marker for stenosis, can be identified, marked, and assessed. Typically performed by a trained operator, now automated approaches have been introduced that can automatically segment and classify the status of the carotid artery intima-media, aiding in the diagnosis of the chances of stroke. In this paper, a new framework based on two components is presented to segment the intima-media layer of the carotid artery to aid in diagnosis of the status. Firstly, the segmentation model is based on an enhanced Unet using multi-scale squeeze and excite operations. Secondly, a novel patch-wise dice loss function is introduced to optimize the normal dice loss function. The obtained results using augmentation on two combined datasets indicate an improvement in different metrics with respect to the state of the art. Notably, 89.4% dice coefficient index and 80.85% IoU, with data augmentation. The source code for the functions discussed in this paper will be available at https://github.com/Vlabgit/MSEUnet.git. • A Multi-scale SE Unet for intima-media segmentation of the carotid artery. • A patch wise dice loss function for learning refined features. • Exploration of size and variety of datasets such as datasets combinations. • High Dice coefficient and IoU compared to the state of the art.