期刊
PHYSICS & IMAGING IN RADIATION ONCOLOGY
卷 12, 期 -, 页码 80-86出版社
ELSEVIER
DOI: 10.1016/j.phro.2019.11.006
关键词
Deep learning; Autosegmentation; MR-only; U-Net; Prostate cancer
资金
- NIH/NCI Cancer Center Support Grant/Core Grant [P30 CA008748]
Background and purpose: Magnetic resonance (MR) only radiation therapy for prostate treatment provides superior contrast for defining targets and organs-at-risk (OARs). This study aims to develop a deep learning model to leverage this advantage to automate the contouring process. Materials and methods: Six structures (bladder, rectum, urethra, penile bulb, rectal spacer, prostate and seminal vesicles) were contoured and reviewed by a radiation oncologist on axial T2-weighted MR image sets from 50 patients, which constituted expert delineations. The data was split into a 40/10 training and validation set to train a two-dimensional fully convolutional neural network, DeepLabV3+, using transfer learning. The T2-weighted image sets were pre-processed to 2D false color images to leverage pre-trained (from natural images) convolutional layers' weights. Independent testing was performed on an additional 50 patient's MR scans. Performance comparison was done against a U-Net deep learning method. Algorithms were evaluated using volumetric Dice similarity coefficient (VDSC) and surface Dice similarity coefficient (SDSC). Results: When comparing VDSC, DeepLabV3+ significantly outperformed U-Net for all structures except urethra (P < 0.001). Average VDSC was 0.93 +/- 0.04 (bladder), 0.83 +/- 0.06 (prostate and seminal vesicles [CTV]), 0.74 +/- 0.13 (penile bulb), 0.82 +/- 0.05 (rectum), 0.69 +/- 0.10 (urethra), and 0.81 +/- 0.1 (rectal spacer). Average SDSC was 0.92 +/- 0.1 (bladder), 0.85 +/- 0.11 (prostate and seminal vesicles [CTV]), 0.80 +/- 0.22 (penile bulb), 0.87 +/- 0.07 (rectum), 0.85 +/- 0.25 (urethra), and 0.83 +/- 0.26 (rectal spacer). Conclusion: A deep learning-based model produced contours that show promise to streamline an MR-only planning workflow in treating prostate cancer.
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