ArticleMedical image analysis2019
A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.
Article in Medical image analysis, 2019. 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.
- Developing a segmentation cascade deep learning network based on automated prompts.Quantitative imaging in medicine and surgery · 2026Article
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Synthetic data in generalizable, learning-based neuroimaging.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.Annals of medicine and surgery (2012) · 2024Article
- Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution.Physics and imaging in radiation oncology · 2023Article
- Pre-trained deep learning models for brain MRI image classification.Frontiers in human neuroscience · 2023Article
- Addressing bias in manual segmentation of spheroid sprouting assays with U-Net.Molecular vision · 2023Article
- Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images.Scientific reports · 2022Article
- DNL-Net: deformed non-local neural network for blood vessel segmentation.BMC medical imaging · 2022Article
- Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image.BMC musculoskeletal disorders · 2022Article
- SOMA: Subject-, object-, and modality-adapted precision atlas approach for automatic anatomy recognition and delineation in medical images.Medical physics · 2021Article
- Segmentation evaluation with sparse ground truth data: Simulating true segmentations as perfect/imperfect as those generated by humans.Medical image analysis · 2021Article
- Analyzing magnetic resonance imaging data from glioma patients using deep learning.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2021Review
- Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling.NeuroImage · 2020Article
- Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation.Frontiers in neuroscience · 2019Article
- Tumor-treating fields dosimetry in glioblastoma: Insights into treatment planning, optimization, and dose-response relationships.Neuro-oncology advancesReview
- Segmentation of Organs and Tumor within Brain Magnetic Resonance Images Using K-Nearest Neighbor Classification.Journal of medical physicsArticle
- Magnetic resonance image-based brain tumour segmentation methods: A systematic review.Digital healthReview
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.
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Registered trials
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.