Towards Robust Cardiac Segmentation using Graph Convolutional Networks

• DOI: 10.48550/arxiv.2310.01210
• Publication Date: 2024-01-01
• Authors: Gilles Van De Vyver; Sarina Thomas; Guy Ben-Yosef; Sindre Hellum Ola...; Håvard Dalen; Lasse Løvstakken; Erik Smistad
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; ultrasound; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; Electrical Engineering and Systems Science - Image and Video Processing; TK1-9971; Machine Learning (cs.LG); robust segmentation; graph convectional network; FOS: Electrical engineering, electronic engineering, information engineering; Electrical engineering. Electronics. Nuclear engineering; Cardiac segmentation

This work has been submitted to the IEEE for possible publication<br/>Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measurements from an echocardiography examination. The U-Net architecture is the current state-of-the-art deep learning architecture for medical segmentation and can segment cardiac structures in real-time with average errors comparable to inter-observer variability. However, this architecture still generates large outliers that are often anatomically incorrect. This work uses the concept of graph convolutional neural networks that predict the contour points of the structures of interest instead of labeling each pixel. We propose a graph architecture that uses two convolutional rings based on cardiac anatomy and show that this eliminates anatomical incorrect multi-structure segmentations on the publicly available CAMUS dataset. Additionally, this work contributes with an ablation study on the graph convolutional architecture and an evaluation of clinical measurements on the clinical HUNT4 dataset. Finally, we propose to use the inter-model agreement of the U-Net and the graph network as a predictor of both the input and segmentation quality. We show this predictor can detect out-of-distribution and unsuitable input images in real-time. Source code is available online: https://github.com/gillesvntnu/GCN_multistructure<br/>