ArticlePhysics and imaging in radiation oncology2023
Towards interactive deep-learning for tumour segmentation in head and neck cancer radiotherapy.
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 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Interactive AI annotation of medical images in a virtual reality environment.International journal of computer assisted radiology and surgery · 2026Article
- Interactive 3D segmentation for primary gross tumor volume in oropharyngeal cancer.Scientific reports · 2025Article
- An interactive deep-learning workflow for head and neck gross tumour volume segmentation.Physics and imaging in radiation oncology · 2025Article
- Understanding and leveraging uncertainties in autosegmentation for radiotherapy.BJR artificial intelligence · 2025Review
- Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- UMamba Adjustment: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and NnU-Net ResEnc Planner.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024Article
- Evolving Horizons in Radiation Therapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification.Advances in radiation oncology · 2024Article
- Accurate object localization facilitates automatic esophagus segmentation in deep learning.Radiation oncology (London, England) · 2024Article
- Clinical acceptability of automatically generated lymph node levels and structures of deglutition and mastication for head and neck radiation therapy.Physics and imaging in radiation oncology · 2024Article
- Validation of a deep-learning segmentation model for adult and pediatric head and neck radiotherapy in different patient positions.Physics and imaging in radiation oncology · 2024Article
- Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images.BJR artificial intelligence · 2024Article
- Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: With deep-learning, gross tumour volume (GTV) auto-segmentation has substantially been improved, but still substantial manual corrections are needed. With interactive deep-learning (iDL), manual corrections can be used to update a deep-learning tool while delineating, minimising the input to achieve acceptable segmentations. We present an iDL tool for GTV segmentation that took annotated slices as input and simulated its performance on a head and neck cancer (HNC) dataset. Materials and methods: Multimodal image data of 204 HNC patients with clinical tumour and lymph node GTV delineations were used. A baseline convolutional neural network (CNN) was trained (n = 107 training, n = 22 validation) and tested (n = 24). Subsequently, user input was simulated on initial test set by replacing one or more of predicted slices with ground truth delineation, followed by re-training the CNN. The objective was to optimise re-training parameters and simulate slice selection scenarios while limiting annotations to maximally-five slices. The remaining 51 patients were used as an independent test set, where Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD Results: Median segmentation accuracy at baseline was DSC = 0.65, MSD = 4.3 mm, HD Conclusions: The presented iDL tool achieved substantial segmentation improvement with only five annotated slices.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.