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Published on: April 9, 2019
Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.
Mohammad-Kasim Fassia1, Adithya Balasubramanian1, Sungmin Woo1
1From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.).
Deep learning algorithms achieve accuracy comparable to expert radiologists for prostate MRI segmentation. Future research should focus on robustness and patient outcomes in clinical settings.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Healthcare
- Oncology
Background:
- Prostate cancer diagnosis relies heavily on Magnetic Resonance Imaging (MRI) for accurate segmentation.
- Deep learning (DL) shows promise for automating prostate MRI segmentation, but its accuracy relative to expert radiologists needs systematic evaluation.
- Understanding the robustness of DL algorithms across diverse datasets and vendors is crucial for clinical adoption.
Approach:
- A systematic review was conducted by querying Embase, PubMed, Scopus, and Web of Science for studies on prostate MRI segmentation using DL.
- 48 English-language articles published up to July 31, 2022, met the inclusion criteria.
- Data extracted included DL algorithm performance, MRI vendor, and training dataset characteristics, with the Dice Similarity Coefficient (DSC) as the primary outcome measure.
Key Points:
- Most published DL algorithms (93%) achieved a DSC of 0.86 or higher for whole prostate gland segmentation, meeting expert level.
- Mean DSCs were 0.79 ± 0.06 for the peripheral zone, 0.87 ± 0.05 for the transition zone, and 0.90 ± 0.04 for the whole prostate gland.
- DL algorithms demonstrated high accuracy across major MRI vendors (GE, Philips, Siemens), with mean DSCs ranging from 0.91 to 0.92.
Conclusions:
- Deep learning algorithms demonstrate accuracy for prostate MRI segmentation that is similar to that of fellowship-trained diagnostic radiologists.
- The performance of DL algorithms is robust across various training data sizes, MRI vendors, and prostate zones.
- Future research should prioritize evaluating the robustness of DL segmentation and its impact on patient outcomes in real-world clinical settings.
