Towards Robust Cardiac Segmentation using Graph Convolutional Networks
This repository is the source code for the training framework of Towards Robust Cardiac Segmentation using Graph Convolutional Networks.
Code of the architecture as a modular entity
If you want to use the architecture in your own project, you can use the architecture as a modular entity as provided in https://github.com/gillesvntnu/GraphBasedSegmentation.git. The code in that repository contains the isolated code for the arhictecture only, so you can insert it in any PyTorch framework.
Real-time demo
For code of the real-time, c++ demo of inter model agreement, see https://github.com/gillesvntnu/GCN_UNET_agreement_demo.git
The GCN and nnU-Net segmentations are shown on the left and right side respectively. The color-coded status bar on top visualizes the agreement between the models. The full demo video is availabel at https://doi.org/10.6084/m9.figshare.24230194.
Quickstart
See QUICKSTART.md/ to get started with the default configuration.
Introduction
Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measure- ments 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 below inter-observer variability. However, this architecture still generates large outliers that are often anatomically incor- rect. This work expands the concept of graph convolutional neural networks that predict the contour points of the struc- tures of interest instead of labelling each pixel. We pro- pose a graph architecture that uses two convolutional rings based on cardiac anatomy and show that this eliminates anatomical incorrect segmentations on the publicly avail- able CAMUS dataset. Additionally, this work contributes with an ablation study on the graph convolutional archi- tecture and an evaluation of clinical measurements on the large 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. For the full article, see Towards Robust Cardiac Segmentation using Graph Convolutional Networks.
Acknowledgements
This work extends the framework provided by
- S. Thomas, A. Gilbert, and G. Ben-Yosef: “Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore https://github.com/guybenyosef/EchoGraphs.git
The code expands the model to multi structure segmentation and provides functionality to convert pixel-wise segmentation maps annotations to clinically motivated keypoint annotations.
Dataset
This repository contains code to train and test the GCN model on the CAMUS dataset. The CAMUS dataset is a publicly available dataset of 500 patients including Apical 2 Chamber (A2C) and Apical 4 Chamber (A4C) views obtained from a GE Vivid E95 ultrasound scanner, equalling 2000 image annotation pairs. The annotations are available as pixel-wise labels of the left ventricle (LV), left atrium (LA), and myocardium (MYO), split into 10 folds for cross-validation The CAMUS dataset is available at https://www.creatis.insa-lyon.fr/Challenge/camus/.
- S. Leclerc, E. Smistad, J. Pedrosa, A. Østvik, F. Cervenansky, F. Espinosa, T. Espeland, E. A. R. Berg, P.-M. Jodoin, T. Grenier et al., “Deep learning for segmentation using an open large-scale dataset in 2d echocardiography,”
Information on the CAMUS cross validation splits can be found in files/listSubGroups.
Architecture
The architecture of the GCN. The CNN encoder transforms the input ultrasound image of width W and height
H to an embedded vector of size X. A dense layer transforms this embedding to an embedding in keypoint space, with 107
keypoints and C1 channels. The decoder consists of a sequence of graph convolutions over these keypoint embeddings. The
final outputs are the 2D coordinates of the keypoints in the image.
Results
Case analysis and comparison of the GCN with displacement method and nnU-Net on CAMUS. The cases are selected based on the overall Dice score between the annotation and the GCN or U-Net segmentations.
Environment
See INSTALL.md/ for environment setup.
Getting stated
See GETTING_STARTED.md to get started with training and testing the GCN model.
Contact
Developer:
gillesvandevyver@hotmail.be
https://www.linkedin.com/in/gilles-van-de-vyver/
Management:
lasse.lovstakken@ntnu.no
erik.smistad@ntnu.no