Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps.
Autor: | Hiremath A; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA. axh672@case.edu., Shiradkar R; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA., Merisaari H; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.; Department of Diagnostic Radiology, University of Turku, Turku, Finland., Prasanna P; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA., Ettala O; Department of Urology, University of Turku and Turku University Hospital, Turku, Finland., Taimen P; Institute of Biomedicine, Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland., Aronen HJ; Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland., Boström PJ; Department of Urology, University of Turku and Turku University Hospital, Turku, Finland., Jambor I; Department of Diagnostic Radiology, University of Turku, Turku, Finland.; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Madabhushi A; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, USA. |
---|---|
Jazyk: | angličtina |
Zdroj: | European radiology [Eur Radiol] 2021 Jan; Vol. 31 (1), pp. 379-391. Date of Electronic Publication: 2020 Jul 23. |
DOI: | 10.1007/s00330-020-07065-4 |
Abstrakt: | Objectives: To evaluate short-term test-retest repeatability of a deep learning architecture (U-Net) in slice- and lesion-level detection and segmentation of clinically significant prostate cancer (csPCa: Gleason grade group > 1) using diffusion-weighted imaging fitted with monoexponential function, ADC Methods: One hundred twelve patients with prostate cancer (PCa) underwent 2 prostate MRI examinations on the same day. PCa areas were annotated using whole mount prostatectomy sections. Two U-Net-based convolutional neural networks were trained on three different ADC Results: For the three ADC Conclusions: For the three ADC Key Points: • For the three ADC |
Databáze: | MEDLINE |
Externí odkaz: |