Abstract
Volumetric image segmentation often relies on voxel-wise classification using 3D convolutional neural networks (CNNs). However, 3D CNNs are inefficient for detecting thin structures that make up a tiny fraction of the entire image volume. We propose a geometric deep learning framework that leverages the representation of the image as a keypoint (KP) cloud and segments it with a graph convolutional network (GCN). From the sparse point segmentations, 3D meshes of the objects are reconstructed to obtain a dense surface. The method is evaluated for the lung fissure segmentation task on two public data sets of thorax CT images and compared to the nnU-Net as the current state-of-the-art 3D CNNbased method. Our method achieves fast inference times through the sparsity of the point cloud representation while maintaining accuracy. We measure a 34× speed-up at 1.5× the nnU-Net’s error with Förstner KPs and a 6× speed-up at 1.3× error with pre-segmentation KPs.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Kaftan, P., Heinrich, M.P., Hansen, L., Rasche, V., Kestler, H.A., Bigalke, A. (2024). Abstracting Volumetric Medical Images with Sparse Keypoints for Efficient Geometric Segmentation of Lung Fissures with a Graph CNN. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_19
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DOI: https://doi.org/10.1007/978-3-658-44037-4_19
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