Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021; 24th International C Marleen de Bruijne,Philippe C. Cattin,Caroli

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0302-9743 ational Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.*.The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are org

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Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-supervised Learningchmarks, including the ones that are unseen during training. Our results show . of the proposed approach across data from different machines and with different SOIs: a major use case of semi-automatic segmentation methods where fully supervised approaches would normally struggle.

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Self-supervised Multi-modal Alignment for Whole Body Medical Imaging unsupervised manner. (iii) Finally, we use these registrations to transfer segmentation maps from the DXA scans to the MR scans where they are used to train a network to segment anatomical regions without requiring ground-truth MR examples. To aid further research, our code is publicly available (.).

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One-Shot Medical Landmark Detectionk detector with those predictions. The effectiveness of CC2D is evaluated on a widely-used public dataset of cephalometric landmark detection, which achieves a competitive detection accuracy of 86.25.01% within 4.0 mm, comparable to the state-of-the-art semi-supervised methods using a lot more than one training image. Our code is available at ..

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SSLP: Spatial Guided Self-supervised Learning on Pathological Imagesxpert annotations. However, the performance of SSL algorithms on WSIs has long lagged behind their supervised counterparts. To close this gap, in this paper, we fully explore the intrinsic characteristics of WSIs and propose SSLP: Spatial Guided Self-supervised Learning on Pathological Images. We ar