ProsRegNet: A Deep Learning Framework for Registration of MRI and Histopathology Images of the Prostate

Autor: Shao, Wei, Banh, Linda, Kunder, Christian A., Fan, Richard E., Soerensen, Simon J. C., Wang, Jeffrey B., Teslovich, Nikola C., Madhuripan, Nikhil, Jawahar, Anugayathri, Ghanouni, Pejman, Brooks, James D., Sonn, Geoffrey A., Rusu, Mirabela

Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed cli

Externí odkaz: http://arxiv.org/abs/2012.00991

Registration of pre-surgical MRI and whole-mount histopathology images in prostate cancer patients with radical prostatectomy via RAPSODI

Autor: Rusu, Mirabela, Kunder, Christian A., Teslovich, Nikola C., Wang, Jeffrey B, Sood, Rewa R., Shao, Wei, Chan, Leo C., West, Robert, Fan, Richard, Ghanouni, Pejman, Brooks, James B., Sonn, Geoffrey A.

Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis. It can spare men with a normal exam from undergoing invasive biopsy while making biopsies more accurate in men with lesions suspicious for cancer. Yet, the sub

Externí odkaz: http://arxiv.org/abs/1907.00324

Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA:an MRI-pathology correlation and deep learning framework: CorrSigNIA: an MRI-pathology correlation and deep learning framework

Autor: Bhattacharya, Indrani, Seetharaman, Arun, Kunder, Christian, Shao, Wei, Chen, Leo C., Soerensen, Simon J.C., Wang, Jeffrey B., Teslovich, Nikola C., Fan, Richard E., Ghanouni, Pejman, Brooks, James D., Sonn, Geoffrey A., Rusu, Mirabela

Publikováno v: Bhattacharya, I, Seetharaman, A, Kunder, C, Shao, W, Chen, L C, Soerensen, S J C, Wang, J B, Teslovich, N C, Fan, R E, Ghanouni, P, Brooks, J D, Sonn, G A & Rusu, M 2022, ' Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA : an MRI-pathology correlation and deep learning framework: CorrSigNIA: an MRI-pathology correlation and deep learning framework ', Medical Image Analysis, vol. 75, 102288 . https://doi.org/10.1016/j.media.2021.102288

Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis and treatment planning. Existing automated methods of prostate cancer detection mo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pure_au_____::55a15b9343066ca4e550c16d430ccfa0
https://pure.au.dk/portal/da/publications/selective-identification-and-localization-of-indolent-and-aggressive-prostate-cancers-via-corrsignia(c5f98adb-d902-4677-94d8-3d563eae80ee).html

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CorrSigNet:Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis

Autor: Bhattacharya, Indrani, Seetharaman, Arun, Shao, Wei, Sood, Rewa, Kunder, Christian A., Fan, Richard E., Soerensen, Simon John Christoph, Wang, Jeffrey B., Ghanouni, Pejman, Teslovich, Nikola C., Brooks, James D., Sonn, Geoffrey A., Rusu, Mirabela

Publikováno v: Bhattacharya, I, Seetharaman, A, Shao, W, Sood, R, Kunder, C A, Fan, R E, Soerensen, S J C, Wang, J B, Ghanouni, P, Teslovich, N C, Brooks, J D, Sonn, G A & Rusu, M 2020, CorrSigNet : Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis . in A L Martel, P Abolmaesumi, D Stoyanov, D Mateus, M A Zuluaga, S K Zhou, D Racoceanu & L Joskowicz (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020-23rd International Conference, Proceedings . Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12262 LNCS, pp. 315-325, 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 04/10/2020 . https://doi.org/10.1007/978-3-030-59713-9_31

Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on MRI, resulting in missed diagnoses and alarming variability in radiolo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pure_au_____::40e73cd0c7eb479a8563f7685aed6084
https://pure.au.dk/portal/da/publications/corrsignet(0e3cc1e6-ef8c-460b-b425-17f14f4f94f7).html