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Random walk based segmentation for the prostate on 3D transrectal ultrasound images.

, , , , , , , , , and . Medical Imaging: Image-Guided Procedures, volume 9786 of SPIE Proceedings, page 978607. SPIE, (2016)

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Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization., , , , , , , , , and . ISBI, page 348-351. IEEE, (2019)Combining population and patient-specific characteristics for prostate segmentation on 3D CT images., , , , , , , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 978427. SPIE, (2016)MRI-TRUS Image Synthesis with Application to Image-Guided Prostate Intervention., , , , , and . SASHIMI@MICCAI, volume 9968 of Lecture Notes in Computer Science, page 157-166. (2016)Learning nonrigid deformations for constrained point-based registration for image-guided MR-TRUS prostate intervention., , , , and . ISBI, page 1592-1595. IEEE, (2015)Atlas-Based Semantic Segmentation of Prostate Zones., , , and . MICCAI (5), volume 13435 of Lecture Notes in Computer Science, page 570-579. Springer, (2022)Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention., , , , , , and . Medical Image Anal., (2017)Integrating Prostate Specific Antigen Density Biomarker Into Deep Learning Prostate MRI Lesion Segmentation Models., , , and . ISBI, page 1-5. IEEE, (2023)Random walk based segmentation for the prostate on 3D transrectal ultrasound images., , , , , , , , , and . Medical Imaging: Image-Guided Procedures, volume 9786 of SPIE Proceedings, page 978607. SPIE, (2016)