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Expert-driven label fusion in multi-atlas-based segmentation of the prostate using weighted atlases.

, , , , , and . Int. J. Comput. Assist. Radiol. Surg., 8 (6): 929-936 (2013)

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Visual Analytics for the Exploration of Tumor Tissue Characterization., , , , , , and . Comput. Graph. Forum, 34 (3): 11-20 (2015)Segmentation of the Prostate in MR Images by Atlas Matching., , , , , and . ISBI, page 1300-1303. IEEE, (2007)Prostate cancer risk assessment using a radiogenomic analysis., , , , , , , , , and . MeMeA, page 1-6. IEEE, (2023)Improving label fusion in multi-atlas based segmentation by locally combining atlas selection and performance estimation., , , , and . Comput. Vis. Image Underst., (2015)Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT., , , and . ISBI, page 577-580. IEEE, (2020)Label fusion in multi-atlas based segmentation with user-defined local weights., , , , and . ISBI, page 1480-1483. IEEE, (2011)Label Fusion Using Performance Estimation with Iterative Label Selection., , , , , and . ISBI, page 1183-1186. IEEE, (2009)Expert-driven label fusion in multi-atlas-based segmentation of the prostate using weighted atlases., , , , , and . Int. J. Comput. Assist. Radiol. Surg., 8 (6): 929-936 (2013)Pseudo-CT image generation from mDixon MRI images using fully convolutional neural networks., , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 10953 of SPIE Proceedings, page 109530Z. SPIE, (2019)Free-form image registration regularized by a statistical shape model: application to organ segmentation in cervical MR., , , , and . Comput. Vis. Image Underst., 117 (9): 1119-1127 (2013)