Semi-Supervised Coronary Vessels Segmentation from Invasive Coronary Angiography with Connectivity-Preserving Loss Function

Abstract

The segmentation of arteries in invasive coronary angiography is necessary to build quantitative models and eventually improve the diagnosis of cardiovascular diseases. Standard segmentation algorithms suffer due to the lack of fully annotated datasets and tend to return disconnected vessels. Thus, we explore a semi-supervised segmentation framework to address these issues. Specifically, we use a student model and a teacher model as the main framework with Nested U-Nets (UNet++) as their backbones. The student model learns by minimizing a segmentation loss between the output and the ground truth, and a consistency loss guided by the uncertainty information. Additionally, a special loss function based on elastic interaction is used to improve the connectivity of arterial branches. We demonstrate the effectiveness of our proposed techniques over 42 labeled and 60 unlabeled samples and find relative improvement of 5.59% for Dice score and 69.99% for Betti number compared to a U-Net.

Semantic segmentation of coronary arteries in computed tomography angiograph: A multi-center, multi-vendor and multi disease study

“…Methods can be designed for a wide range of applications, or denotes methods speci cally developed and optimized for coronary vessel segmentation. [11]. The second task pertains to semantic segmentation [7], there are some works that have focused on extracting semantic features within a segmentation framework.…”

“…A modi ed Mask-Region based CNN model is utilized to segment the coronary arteries, identify coronary plaque candidates, and extract the image patch with great computational e ciency. He et al [11] proposed a new semi-supervised framework for whole coronary arterial tree segmentation in invasive coronary angiography. A mean Teacher (MT) model was used as the main framework with Nested U-Net (UNet++) as its backbone.…”