Spatial Correspondence between Graph Neural Network-Segmented Images
Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges. One such type of graph is predefined with fixed size and connectivity to represent a reference of anatomical regions of interest, thus know...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
11.03.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Graph neural networks (GNNs) have been proposed for medical image
segmentation, by predicting anatomical structures represented by graphs of
vertices and edges. One such type of graph is predefined with fixed size and
connectivity to represent a reference of anatomical regions of interest, thus
known as templates. This work explores the potentials in these GNNs with common
topology for establishing spatial correspondence, implicitly maintained during
segmenting two or more images. With an example application of registering local
vertebral sub-regions found in CT images, our experimental results showed that
the GNN-based segmentation is capable of accurate and reliable localization of
the same interventionally interesting structures between images, not limited to
the segmentation classes. The reported average target registration errors of
2.2$\pm$1.3 mm and 2.7$\pm$1.4 mm, for aligning holdout test images with a
reference and for aligning two test images, respectively, were by a
considerable margin lower than those from the tested non-learning and
learning-based registration algorithms. Further ablation studies assess the
contributions towards the registration performance, from individual components
in the originally segmentation-purposed network and its training algorithm. The
results highlight that the proposed segmentation-in-lieu-of-registration
approach shares methodological similarities with existing registration methods,
such as the use of displacement smoothness constraint and point distance
minimization albeit on non-grid graphs, which interestingly yielded benefits
for both segmentation and registration. We, therefore, conclude that the
template-based GNN segmentation can effectively establish spatial
correspondence in our application, without any other dedicated registration
algorithms. |
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DOI: | 10.48550/arxiv.2303.06550 |