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Designing image segmentation studies: Statistical power, sample size and reference standard quality.

, , , and . Medical Image Anal., (2017)

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Designing image segmentation studies: Statistical power, sample size and reference standard quality., , , and . Medical Image Anal., (2017)Registration Accuracy: How Good Is Good Enough? A Statistical Power Calculation Incorporating Image Registration Uncertainty., , and . MICCAI (2), volume 7511 of Lecture Notes in Computer Science, page 643-650. Springer, (2012)Multiparametric MR imaging of prostate cancer foci: assessing the detectability and localizability of Gleason 7 peripheral zone cancers based on image contrasts., , , , , , , , , and 6 other author(s). Medical Imaging: Digital Pathology, volume 9041 of SPIE Proceedings, page 90410N. SPIE, (2014)Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration., , , , , , and . Medical Image Anal., (2019)No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks., , , , , , and . CoRR, (2020)NiftyNet: a deep-learning platform for medical imaging., , , , , , , , , and 6 other author(s). CoRR, (2017)Tissue block MRI for slice orientation-independent registration of digital histology images to ex vivo MRI of the prostate., , , , , , and . ISBI, page 566-569. IEEE, (2011)Label-driven weakly-supervised learning for multimodal deformarle image registration., , , , , , , , , and . ISBI, page 1070-1074. IEEE, (2018)2D-3D Registration Accuracy Estimation for Optimised Planning of Image-Guided Pancreatobiliary Interventions., , , , , , , and . MICCAI (1), volume 9900 of Lecture Notes in Computer Science, page 516-524. (2016)Brain midline shift detection and quantification by a cascaded deep network pipeline on non-contrast computed tomography scans., , , , , , , , , and 6 other author(s). ICCVW, page 487-495. IEEE, (2021)