Towards Robust Cardiac Segmentation using Graph Convolutional Networks

TL;DR

This work proposes a graph architecture that uses two convolutional rings based on cardiac anatomy that eliminates anatomical incorrect multi-structure segmentations on the publicly available CAMUS dataset and shows this predictor can detect out-of-distribution and unsuitable input images in real-time.

Abstract

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

Figures (12)

• Figure 1: Though U-Nets can achieve a high average Dice accuracy on large datasets (> 0.94), they can still produce anatomical incorrect results as shown here to the left, with multiple atria disconnected from the LV and an incoherent myocardium around the LV, none of which are anatomically plausible. The anatomical correct output of the proposed graph convolutional network (GCN) is shown to the right.
• Figure 2: 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.
• Figure 3: Schematic diagram showing the preprocessing to transform pixel labels to keypoints positions. A and B are the base points of the LV. C and D are the base points of the MYO. E, F, and G are the apexes of the LV, MYO, and LA respectively.
• Figure 4: Schematic diagram showing the multi-structure convolution. $q_{1..n}$ are zero-padded epicardium keypoints, $p_{1..n}$ are endocardium and left atrium keypoints. The highlighted points are used as input to update the embedding of the purple keypoint, with w the primary receptive field and v the secondary receptive field.For illustrative purposes, the diagram does not show the actual number of keypoints used in this work.
• Figure 5: Graphical representation of the GCN - U-Net cascade. The cascade model concatenates the initial segmentation produced by the GCN with the original ultrasound input and feeds this to the U-Net to produce the final, refined segmentation. The segmentations shown are only illustrative.