ArticleMedical physics2020
Fast contour propagation for MR-guided prostate radiotherapy using convolutional neural networks.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).La Radiologia medica · 2024Pooled it
- Automated workflow for target volume propagation in online adaptive magnetic resonance guided radiotherapy for lung lesions using foundation models.Physics and imaging in radiation oncology · 2026Article
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy.Zeitschrift fur medizinische Physik · 2024Article
- Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions.Advances in radiation oncology · 2024Review
- Prior knowledge based deep learning auto-segmentation in magnetic resonance imaging-guided radiotherapy of prostate cancer.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
- Integration of operator-validated contours in deformable image registration for dose accumulation in radiotherapy.Physics and imaging in radiation oncology · 2023Article
- An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.Journal of digital imaging · 2023Article
- Review
- Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy.Physics and imaging in radiation oncology · 2022Article
- Clinical rationale forPhysics and imaging in radiation oncology · 2022Article
- Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization.Sensors (Basel, Switzerland) · 2021Article
- Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy.Frontiers in oncology · 2021Review
- MR-guided proton therapy: a review and a preview.Radiation oncology (London, England) · 2020Review
- Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy.Radiation oncology (London, England) · 2020Article
- Medical physics challenges in clinical MR-guided radiotherapy.Radiation oncology (London, England) · 2020Review
- Radiobiological Modeling with Monte Carlo Tools - Simulating Cellular Responses to Ionizing Radiation.Technology in cancer research & treatmentReview
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo quickly and automatically propagate organ contours from pretreatment to fraction images in magnetic resonance (MR)-guided prostate external-beam radiotherapy.
methodsFive prostate cancer patients underwent 20 fractions of image-guided external-beam radiotherapy on a 1.5 T MR-Linac system. For each patient, a pretreatment T2-weighted three-dimensional (3D) MR imaging (MRI) scan was used to delineate the clinical target volume (CTV) contours. The same scan was repeated during each fraction, with the CTV contour being manually adapted if necessary. A convolutional neural network (CNN) was trained for combined image registration and contour propagation. The network estimated the propagated contour and a deformation field between the two input images. The training set consisted of a synthetically generated ground truth of randomly deformed images and prostate segmentations. We performed a leave-one-out cross-validation on the five patients and propagated the prostate segmentations from the pretreatment to the fraction scans. Three variants of the CNN, aimed at investigating supervision based on optimizing segmentation overlap, optimizing the registration, and a combination of the two were compared to results of the open-source deformable registration software package Elastix.
resultsThe neural networks trained on segmentation overlap or the combined objective achieved significantly better Hausdorff distances between predicted and ground truth contours than Elastix, at the much faster registration speed of 0.5 s. The CNN variant trained to optimize both the prostate overlap and deformation field, and the variant trained to only maximize the prostate overlap, produced the best propagation results.
conclusionsA CNN trained on maximizing prostate overlap and minimizing registration errors provides a fast and accurate method for deformable contour propagation for prostate MR-guided radiotherapy.
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