Abstract
Magnetic Resonance Imaging (MRI) has become an essential tool for clinical knee examinations. In clinical practice, knee scans are acquired from multiple views with stacked 2D slices, ensuring diagnosis accuracy while saving scanning time. However, obtaining fine 3D knee segmentation from multi-view 2D scans is challenging, which is yet necessary for morphological analysis. Moreover, radiologists need to annotate the knee segmentation in multiple 2D scans for medical studies, bringing additional labor. In this paper, we propose the Cross-view Aligned Segmentation Network (CAS-Net) to produce 3D knee segmentation from multi-view 2D MRI scans and annotations of sagittal views only. Specifically, a knee graph representation is firstly built in a 3D isotropic space after the super-resolution of multi-view 2D scans. Then, we utilize a graph-based network to segment individual multi-view patches along the knee surface, and piece together these patch segmentations into a complete knee segmentation with help of the knee graph. Experiments conducted on the Osteoarthritis Initiative (OAI) dataset demonstrate the validity of the CAS-Net to generate accurate 3D segmentation.
Z. Zhuang, X. Wang and S. Wang—These authors contributed equally to this work.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 62001292), and partially supported by the National Natural Science Foundation of China (U22A20283), and the Interdisciplinary Program of Shanghai Jiao Tong University (No. YG2023LC07).
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Zhuang, Z. et al. (2023). CAS-Net: Cross-View Aligned Segmentation by Graph Representation of Knees. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Springer, Cham. https://doi.org/10.1007/978-3-031-43901-8_11
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