Radiation and Cancer Physics

PV 03 - Poster Viewing Q&A - Session 3

TU_45_3832 - Evaluation of a Deep Learning based Auto-segmentation Tool for Online Adaptive Radiation Therapy

Tuesday, September 17
1:00 PM - 2:15 PM
Location: ASTRO Innovation Hub

Evaluation of a Deep Learning based Auto-segmentation Tool for Online Adaptive Radiation Therapy
A. Amjad1, D. Thill2, C. A. F. Lawton1, N. O'Connell2, W. A. Hall1, and A. Li1; 1Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 2Global Eng. NA Elekta, Maryland Heights, MO

Purpose/Objective(s): Fast and consistent segmentation of tumors and organs at risk on daily images is vital for online adaptive radiation therapy (OART). For this purpose, most sought-after method to-date is auto-segmentation, built upon machine learning algorithms. This study evaluates the performance of a Deep Learning based automatic segmentation model for OART.

Materials/Methods: A research version of Deep Learning (DL) auto-segmentation model, using Deep Convolutional Neural Network (DCNN) for auto-segmentation on CT and MRI is evaluated based on CT and MRI of randomly selected 50 prostate cancer patients. The model’s architecture belongs to Fully Convolutional Neural Network, with end-to-end mapping and sequential application of 2.5D and 3D models for 3D image segmentation. Enhanced convergence is achieved via short- and long-range residue and U-Net skip connections. Data augmentation through random transformations of input images and respective segmentation maps was used in model training. For testing CT sets, contours of prostate, rectum and bladder were drawn manually based on the CT and MRI registration by a physician or were verified by physician or physician dictated criteria. The clinical viability of the DL contours was checked by qualitative and quantitative comparison with manuals, ground truth contours, and AAPM TG-132 accuracy tolerance recommendations for Dice Similarity Coefficient (DSC) > 0.8 to 0.9 and Mean Distance to Agreement (MDA) < 2 to 3 mm were enforced. The auto-segmentation tool was run on an Intel Xeon CPU (2.4 GHz, 64 GB RAM). The computing times for all the auto-segmentation tests were recorded.

Results: For the cases tested, 98% of the auto-segmented contours remained within TG-132 dictated tolerances indicating clinical acceptability of the contours. The mean and standard deviations of DSC, MDA and Hausdorff Distance (HD) values were tabulated and were found to be (0.87 ± 0.037, 1.38 ± 0.393, 7.5 ± 3.07), (0.89 ± 0.053, 1.13 ± 0.752, 11.2 ± 6.37) and (0.95 ± 0.019, 0.91 ± 0.413, 6.86 ± 5.04) for prostate, rectum and bladder, respectively. A detailed slice-by-slice visual inspection indicated that approximately 20% of the DL contours were fully acceptable, requiring no edits. For the rest, accommodation of anatomical challenges, like abnormal bladder shapes and indistinct organ boundaries (35%), or edits subjective to physician’s discretion, required minor manual editing, with average editing time < 2 minutes. On average the execution of the auto-segmentations took about 0.5 minutes.

Conclusion: The qualitative and quantitative evaluation of the newly developed DL-based auto-segmentation model prove it to be accurate and efficient, with clinically acceptable structures for male pelvis. The auto-segmentation tool can be used to generate contours for male pelvic organs with or without minor editing within 3 minutes, particularly applicable for online adaptive replanning.

Author Disclosure: A. Amjad: Research Grant; MCW Fotsch foundation, MCWCC/FH Foundation, Elekta. D. Thill: None. C.A. Lawton: None. N. O'Connell: None. W.A. Hall: Research Grant; Elekta, National Cancer Institute, American Cancer Society. A. Li: None.

Asma Amjad, PhD

Medical College of Wisconsin

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