Patient-Specific Hyperparameter Optimization of a Deep Learning-Based Tumor Autocontouring Algorithm on 2D Liver, Prostate, and Lung Cine MR Images: A Pilot Study
Linear accelerator–magnetic resonance (linac-MR) hybrid systems allow for real-time magnetic resonance imaging (MRI)-guided radiotherapy for more accurate dose delivery to the tumor and improved sparing of the adjacent healthy tissues. However, for real-time tumor detection, it is unfeasible for a h...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1999-4893/18/4/233 |
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| author | Gawon Han Keith Wachowicz Nawaid Usmani Don Yee Jordan Wong Arun Elangovan Jihyun Yun B. Gino Fallone |
| author_facet | Gawon Han Keith Wachowicz Nawaid Usmani Don Yee Jordan Wong Arun Elangovan Jihyun Yun B. Gino Fallone |
| author_sort | Gawon Han |
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| description | Linear accelerator–magnetic resonance (linac-MR) hybrid systems allow for real-time magnetic resonance imaging (MRI)-guided radiotherapy for more accurate dose delivery to the tumor and improved sparing of the adjacent healthy tissues. However, for real-time tumor detection, it is unfeasible for a human expert to manually contour (gold standard) the tumor at the fast imaging rate of a linac-MR. This study aims to develop a neural network-based tumor autocontouring algorithm with patient-specific hyperparameter optimization (HPO) and to validate its contouring accuracy using in vivo MR images of cancer patients. Two-dimensional (2D) intrafractional MR images were acquired at 4 frames/s using 3 tesla (T) MRI from 11 liver, 24 prostate, and 12 lung cancer patients. A U-Net architecture was applied for tumor autocontouring and was further enhanced by implementing HPO using the Covariance Matrix Adaptation Evolution Strategy. Six hyperparameters were optimized for each patient, for which intrafractional images and experts’ manual contours were input into the algorithm to find the optimal set of hyperparameters. For evaluation, Dice’s coefficient (DC), centroid displacement (CD), and Hausdorff distance (HD) were computed between the manual contours and autocontours. The performance of the algorithm was benchmarked against two standardized autosegmentation methods: non-optimized U-Net and nnU-Net. For the proposed algorithm, the mean (standard deviation) DC, CD, and HD of the 47 patients were 0.92 (0.04), 1.35 (1.03), and 3.63 (2.17) mm, respectively. Compared to the two benchmarking autosegmentation methods, the proposed algorithm achieved the best overall performance in terms of contouring accuracy and speed. This work presents the first tumor autocontouring algorithm applicable to the intrafractional MR images of liver and prostate cancer patients for real-time tumor-tracked radiotherapy. The proposed algorithm performs patient-specific HPO, enabling accurate tumor delineation comparable to that of experts. |
| format | Article |
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| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-05ae453f46a844b59f91fa8ff48c1fef2025-08-20T02:24:43ZengMDPI AGAlgorithms1999-48932025-04-0118423310.3390/a18040233Patient-Specific Hyperparameter Optimization of a Deep Learning-Based Tumor Autocontouring Algorithm on 2D Liver, Prostate, and Lung Cine MR Images: A Pilot StudyGawon Han0Keith Wachowicz1Nawaid Usmani2Don Yee3Jordan Wong4Arun Elangovan5Jihyun Yun6B. Gino Fallone7Medical Physics Division, Department of Oncology, University of Alberta, 11560 University Avenue, Edmonton, AB T6G 1Z2, CanadaMedical Physics Division, Department of Oncology, University of Alberta, 11560 University Avenue, Edmonton, AB T6G 1Z2, CanadaDepartment of Radiation Oncology, Cross Cancer Institute, 11560 University Avenue, Edmonton, AB T6G 1Z2, CanadaDepartment of Radiation Oncology, Cross Cancer Institute, 11560 University Avenue, Edmonton, AB T6G 1Z2, CanadaDepartment of Radiation Oncology, Cross Cancer Institute, 11560 University Avenue, Edmonton, AB T6G 1Z2, CanadaDepartment of Radiation Oncology, Cross Cancer Institute, 11560 University Avenue, Edmonton, AB T6G 1Z2, CanadaMedical Physics Division, Department of Oncology, University of Alberta, 11560 University Avenue, Edmonton, AB T6G 1Z2, CanadaMedical Physics Division, Department of Oncology, University of Alberta, 11560 University Avenue, Edmonton, AB T6G 1Z2, CanadaLinear accelerator–magnetic resonance (linac-MR) hybrid systems allow for real-time magnetic resonance imaging (MRI)-guided radiotherapy for more accurate dose delivery to the tumor and improved sparing of the adjacent healthy tissues. However, for real-time tumor detection, it is unfeasible for a human expert to manually contour (gold standard) the tumor at the fast imaging rate of a linac-MR. This study aims to develop a neural network-based tumor autocontouring algorithm with patient-specific hyperparameter optimization (HPO) and to validate its contouring accuracy using in vivo MR images of cancer patients. Two-dimensional (2D) intrafractional MR images were acquired at 4 frames/s using 3 tesla (T) MRI from 11 liver, 24 prostate, and 12 lung cancer patients. A U-Net architecture was applied for tumor autocontouring and was further enhanced by implementing HPO using the Covariance Matrix Adaptation Evolution Strategy. Six hyperparameters were optimized for each patient, for which intrafractional images and experts’ manual contours were input into the algorithm to find the optimal set of hyperparameters. For evaluation, Dice’s coefficient (DC), centroid displacement (CD), and Hausdorff distance (HD) were computed between the manual contours and autocontours. The performance of the algorithm was benchmarked against two standardized autosegmentation methods: non-optimized U-Net and nnU-Net. For the proposed algorithm, the mean (standard deviation) DC, CD, and HD of the 47 patients were 0.92 (0.04), 1.35 (1.03), and 3.63 (2.17) mm, respectively. Compared to the two benchmarking autosegmentation methods, the proposed algorithm achieved the best overall performance in terms of contouring accuracy and speed. This work presents the first tumor autocontouring algorithm applicable to the intrafractional MR images of liver and prostate cancer patients for real-time tumor-tracked radiotherapy. The proposed algorithm performs patient-specific HPO, enabling accurate tumor delineation comparable to that of experts.https://www.mdpi.com/1999-4893/18/4/233autocontouringintrafractional motion managementtumor trackinglinac-MR hybridMRI guidanceradiotherapy |
| spellingShingle | Gawon Han Keith Wachowicz Nawaid Usmani Don Yee Jordan Wong Arun Elangovan Jihyun Yun B. Gino Fallone Patient-Specific Hyperparameter Optimization of a Deep Learning-Based Tumor Autocontouring Algorithm on 2D Liver, Prostate, and Lung Cine MR Images: A Pilot Study Algorithms autocontouring intrafractional motion management tumor tracking linac-MR hybrid MRI guidance radiotherapy |
| title | Patient-Specific Hyperparameter Optimization of a Deep Learning-Based Tumor Autocontouring Algorithm on 2D Liver, Prostate, and Lung Cine MR Images: A Pilot Study |
| title_full | Patient-Specific Hyperparameter Optimization of a Deep Learning-Based Tumor Autocontouring Algorithm on 2D Liver, Prostate, and Lung Cine MR Images: A Pilot Study |
| title_fullStr | Patient-Specific Hyperparameter Optimization of a Deep Learning-Based Tumor Autocontouring Algorithm on 2D Liver, Prostate, and Lung Cine MR Images: A Pilot Study |
| title_full_unstemmed | Patient-Specific Hyperparameter Optimization of a Deep Learning-Based Tumor Autocontouring Algorithm on 2D Liver, Prostate, and Lung Cine MR Images: A Pilot Study |
| title_short | Patient-Specific Hyperparameter Optimization of a Deep Learning-Based Tumor Autocontouring Algorithm on 2D Liver, Prostate, and Lung Cine MR Images: A Pilot Study |
| title_sort | patient specific hyperparameter optimization of a deep learning based tumor autocontouring algorithm on 2d liver prostate and lung cine mr images a pilot study |
| topic | autocontouring intrafractional motion management tumor tracking linac-MR hybrid MRI guidance radiotherapy |
| url | https://www.mdpi.com/1999-4893/18/4/233 |
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