A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.
Mikael Agn,Per Munck af Rosenschöld,Oula Puonti,Michael Lundemann,Laura Mancini,Anastasia Papadaki,Steffi Thust,John Ashburner,Ian Law,Koen Van Leemput +9 more
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TLDR
In this paper, a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines is presented.About:
This article is published in Medical Image Analysis.The article was published on 2019-05-01 and is currently open access. It has received 35 citations till now. The article focuses on the topics: Radiation treatment planning & Radiation therapy.read more
Citations
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Journal ArticleDOI
Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer.
M. Kosmin,Joseph R. Ledsam,Bernardino Romera-Paredes,R. Mendes,S. Moinuddin,D. de Souza,L. Gunn,Christopher Kelly,Cian Hughes,Alan Karthikesalingam,Christopher M. Nutting,Ricky A. Sharma,Ricky A. Sharma +12 more
TL;DR: The current state-of-the-art for AS for HNC radiotherapy is outlined in order to predict how this will rapidly change with the introduction of artificial intelligence, specifically on delineation accuracy and time saving.
Journal ArticleDOI
Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods.
TL;DR: This review systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date and provided critical discussions and recommendations from various perspectives.
Journal ArticleDOI
Test-time adaptable neural networks for robust medical image segmentation
TL;DR: In this article, a concatenation of two sub-networks, a relatively shallow image normalization network and a deep CNN segmentation network, is proposed for medical image segmentation.
Journal ArticleDOI
Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling.
Oula Puonti,Oula Puonti,Koen Van Leemput,Koen Van Leemput,Guilherme B. Saturnino,Guilherme B. Saturnino,Hartwig R. Siebner,Hartwig R. Siebner,Kristoffer Hougaard Madsen,Kristoffer Hougaard Madsen,Axel Thielscher,Axel Thielscher +11 more
TL;DR: A new method, called CHARM, for automated segmentation of fifteen different head tissues from magnetic resonance (MR) scans is presented, which compares favorably to two freely available software tools on a five-tissue segmentation task, while obtaining reasonable segmentation accuracy over all fifteen tissues.
Proceedings ArticleDOI
Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search
Dazhou Guo,Dakai Jin,Zhuotun Zhu,Tsung-Ying Ho,Adam P. Harrison,Chun-Hung Chao,Jing Xiao,Le Lu +7 more
TL;DR: Extensive 4-fold cross-validation on 142 H&N cancer patients with 42 manually labeled OARs, the most comprehensive OAR dataset to date, demonstrates that both pipeline- and NAS-stratification significantly improves quantitative performance over the state-of-the-art.
References
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Journal ArticleDOI
Radiotherapy planning for glioblastoma based on a tumor growth model: improving target volume delineation
Jan Unkelbach,Bjoern H. Menze,Bjoern H. Menze,Ender Konukoglu,Florian Dittmann,Matthieu Le,Matthieu Le,Nicholas Ayache,Helen A. Shih +8 more
TL;DR: The tumor growth model provides a method to account for anisotropic growth patterns of glioma, and may therefore provide a tool to make target delineation more objective and automated.
Journal ArticleDOI
Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.
Nicolas Sauwen,Marjan Acou,S Van Cauter,Diana M. Sima,Jelle Veraart,Frederik Maes,Uwe Himmelreich,Eric Achten,S. Van Huffel +8 more
TL;DR: It is demonstrated that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.
Book ChapterDOI
Brain Atlas Deformation in the Presence of Large Space-occupying Tumors
TL;DR: Results indicate that the method proposed can be used to automatically segment structures of interest in brains with gross deformation and potential areas of application include automatic labeling of critical structures for radiation therapy and presurgical planning.
Journal ArticleDOI
Impact of [18F]-fluoro-ethyl-tyrosine PET imaging on target definition for radiation therapy of high-grade glioma.
Per Munck af Rosenschöld,Junia Costa,Svend Aage Engelholm,Michael Lundemann,Ian Law,Lars Ohlhues,S.A. Engelholm +6 more
TL;DR: With an unchanged CTV margin and by including FET-PET for gross tumor volume definition, the CTV will increase moderately for most patients, and quite substantially for a minority of patients.
Journal ArticleDOI
An atlas-navigated optimal medial axis and deformable model algorithm (NOMAD) for the segmentation of the optic nerves and chiasm in MR and CT images☆
Jack H. Noble,Benoit M. Dawant +1 more
TL;DR: A novel method for automatically localizing the optic nerves and chiasm using a tubular structure localization algorithm in which a statistical model and image registration are used to incorporate a priori local intensity and shape information.