ArticlePhysics and imaging in radiation oncology2023
Deep learning for automated contouring of neurovascular structures on magnetic resonance imaging for prostate cancer patients.
Article in Physics and imaging in radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Deep learning-enhanced image registration for accelerating daily adaptive magnetic resonance imaging-guided prostate radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Preoperative dual-ROI mpMRI radiomics integrating intratumoral and periprostatic adipose tissue features with clinical factors for predicting positive surgical margins after radical prostatectomy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- The Emerging Role of Multimodal Artificial Intelligence in Urological Surgery.Current oncology (Toronto, Ont.) · 2025Review
- Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures.Physics and imaging in radiation oncology · 2025Article
- Implementation and clinical evaluation of an in-house thoracic auto-segmentation model for 0.35 T magnetic resonance imaging guided radiotherapy.Physics and imaging in radiation oncology · 2025Article
- ASO Author Reflections: AI-driven Personalization in Digital Medicine and Addressing Clinical Bottlenecks: Insights from a Prostate Cancer Application.Annals of surgical oncology · 2025Article
- Artificial Intelligence-Assisted Segmentation of Prostate Tumors and Neurovascular Bundles: Applications in Precision Surgery for Prostate Cancer.Annals of surgical oncology · 2025Article
- Feasibility and time gain of implementing artificial intelligence-based delineation tools in daily magnetic resonance image-guided adaptive prostate cancer radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer.Physics and imaging in radiation oncology · 2024Article
- Automated pelvic MRI measurements associated with urinary incontinence for prostate cancer patients undergoing radical prostatectomy.European radiology experimental · 2024Article
- Impact of daily plan adaptation on accumulated doses in ultra-hypofractionated magnetic resonance-guided radiation therapy of prostate cancer.Physics and imaging in radiation oncology · 2024Article
- Re. van den Berg et al, Deep learning for automated contouring of neurovascular structures on magnetic resonance imaging for prostate cancer patients.Physics and imaging in radiation oncology · 2023Article
- In response to Grivas et al.Physics and imaging in radiation oncology · 2023Article
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10 authors.
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Abstract
Background and purpose: Manual contouring of neurovascular structures on prostate magnetic resonance imaging (MRI) is labor-intensive and prone to considerable interrater disagreement. Our aim is to contour neurovascular structures automatically on prostate MRI by deep learning (DL) to improve workflow and interrater agreement. Materials and methods: Segmentation of neurovascular structures was performed on pre-treatment 3.0 T MRI data of 131 prostate cancer patients (training [n = 105] and testing [n = 26]). The neurovascular structures include the penile bulb (PB), corpora cavernosa (CCs), internal pudendal arteries (IPAs), and neurovascular bundles (NVBs). Two DL networks, nnU-Net and DeepMedic, were trained for auto-contouring on prostate MRI and evaluated using volumetric Dice similarity coefficient (DSC), mean surface distances (MSD), Hausdorff distances, and surface DSC. Three radiation oncologists evaluated the DL-generated contours and performed corrections when necessary. Interrater agreement was assessed and the time required for manual correction was recorded. Results: nnU-Net achieved a median DSC of 0.92 (IQR: 0.90-0.93) for the PB, 0.90 (IQR: 0.86-0.92) for the CCs, 0.79 (IQR: 0.77-0.83) for the IPAs, and 0.77 (IQR: 0.72-0.81) for the NVBs, which outperformed DeepMedic for each structure (p < 0.03). nnU-Net showed a median MSD of 0.24 mm for the IPAs and 0.71 mm for the NVBs. The median interrater DSC ranged from 0.93 to 1.00, with the majority of cases (68.9%) requiring manual correction times under two minutes. Conclusions: DL enables reliable auto-contouring of neurovascular structures on pre-treatment MRI data, easing the clinical workflow in neurovascular-sparing MR-guided radiotherapy.
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