Autoprostate: Towards automated reporting of prostate mri for prostate cancer assessment using deep learning

Pritesh Mehta*, Michela Antonelli, Saurabh Singh, Natalia Grondecka, Edward W. Johnston, Hashim U. Ahmed, Mark Emberton, Shonit Punwani, Sébastien Ourselin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCaSegmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and ReportGenerator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experi-ment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections.

Original languageEnglish
Article number6138
Issue number23
Publication statusE-pub ahead of print - 6 Dec 2021


  • Automatic report
  • Computer-aided diagnosis
  • Convolutional neural network
  • Deep learning
  • Lesion classification
  • Lesion detection
  • Magnetic resonance imaging
  • Prostate cancer
  • Segmentation


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