ArticlePhysics and imaging in radiation oncology2022
Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy.
Article in Physics and imaging in radiation oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Cross-fraction prior learning for scalable organ-at-risk segmentation in abdominal MR-guided radiotherapy.Medical physics · 2026Article
- Patient-specific deep learning tracking for real-time 2D pancreas localisation in kV-guided radiotherapy.Physics and imaging in radiation oncology · 2025Article
- A general model for head and neck auto-segmentation with patient pre-treatment imaging during adaptive radiation therapy.Medical physics · 2025Article
- Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.Medical physics · 2025Article
- Enhancing patient-specific deep learning based segmentation for abdominal magnetic resonance imaging-guided radiation therapy: A framework conditioned on prior segmentation.Physics and imaging in radiation oncology · 2025Article
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Incorporating patient-specific information for the development of rectal tumor auto-segmentation models for online adaptive magnetic resonance Image-guided radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions.Advances in radiation oncology · 2024Review
- Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy.Zeitschrift fur medizinische Physik · 2024Article
- Intra-fractional geometric and dose/volume metric variations of magnetic resonance imaging-guided stereotactic radiotherapy of prostate bed after radical prostatectomy.Physics and imaging in radiation oncology · 2024Article
- Comparing multi-image and image augmentation strategies for deep learning-based prostate segmentation.Physics and imaging in radiation oncology · 2024Article
- Full daily re-optimization improves plan quality during online adaptive radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Adaptation Time as a Determinant of the Dosimetric Effectiveness of Online Adaptive Radiotherapy for Bladder Cancer.Cancers · 2023Article
- Prior knowledge based deep learning auto-segmentation in magnetic resonance imaging-guided radiotherapy of prostate cancer.Physics and imaging in radiation oncology · 2023Article
- A network score-based metric to optimize the quality assurance of automatic radiotherapy target segmentations.Physics and imaging in radiation oncology · 2023Article
- Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors.Radiation oncology (London, England) · 2023Article
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3 authors.
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Abstract
Background and Purpose: Treatments on combined Magnetic Resonance (MR) scanners and Linear Accelerators (Linacs) for radiotherapy, called MR-Linacs, often require daily contouring. Currently, deformable image registration (DIR) algorithms propagate contours from reference scans, however large shape and size changes can be troublesome. Artificial neural network (ANN) based contouring may alleviate this issue, however generally requires large datasets for training. Mitigating the problem of scarcity of data, we propose patient specific networks trained on a single dataset for each patient, for contouring onto the following datasets in an adaptive MR-Linacworkflow. Materials and Methods: MR-scans from 17 prostate patients treated on an MR-Linac with contours of Clinical Target Volume (CTV), bladder and rectum were utilized. U-net shaped models were trained based on the image from the first fraction of each patient, and subsequently applied onto the following treatment images. Results were compared with manual contours in terms of the Dice coefficient and Added Path Length (APL). As benchmark, contours propagated through the clinical DIR algorithm were similarly evaluated. Results: In Dice coefficient the ANN output was 0.92 ± 0.03, 0.93 ± 0.07 and 0.84 ± 0.10 while for DIR 0.95 ± 0.03, 0.93 ± 0.08, 0.88 ± 0.06 for CTV, bladder and rectum respectively. Similarly, APL where 3109 ± 1642, 7250 ± 4234 and 5041 ± 2666 for ANN and 1835 ± 1621, 7236 ± 4287 and 4170 ± 2920 voxels for DIR. Conclusions: Patient specific ANN models trained on images from the first fraction of a prostate MR-Linac treatment showed similar accuracy when applied to the subsequent fraction images as a clinically implemented DIR method.
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