Open AccessDeep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on Magnetic Resonance Imaging for Targeted Biopsy
Author(s) -
Simon John Christoph Soerensen,
Richard E. Fan,
Arun Seetharaman,
Leo Chen,
Wei Shao,
Indrani Bhattacharya,
Yonghun Kim,
Rewa Sood,
Michael Borre,
Benjamin I. Chung,
Katherine To’o,
Mirabela Rusu,
Geoffrey A. Sonn
Publication year - 2021
Publication title -
the journal of urology/the journal of urology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.402
H-Index - 256
eISSN - 1527-3792
pISSN - 0022-5347
DOI - 10.1097/ju.0000000000001783
Subject(s) - medicine , magnetic resonance imaging , prostate gland , prostate , prostate biopsy , biopsy , radiology , cancer
Targeted biopsy improves prostate cancer diagnosis. Accurate prostate segmentation on magnetic resonance imaging (MRI) is critical for accurate biopsy. Manual gland segmentation is tedious and time-consuming. We sought to develop a deep learning model to rapidly and accurately segment the prostate on MRI and to implement it as part of routine magnetic resonance-ultrasound fusion biopsy in the clinic.