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 Article
Automated segmentation of MS lesions from multi-channel MR images
Koen Van Leemput,Frederik Maes,Fernando Bello,Dirk Vandermeulen,Alan C. F. Colchester,Paul Suetens +5 more
TL;DR: In this paper, a fully automated model-based method for segmentation of multiple sclerosis lesions from multi-channel MR images is described, which simultaneously corrects for MR field inhomogeneities, estimates tissue class distribution parameters and classifies the image voxels.
Journal ArticleDOI
Sampling image segmentations for uncertainty quantification.
TL;DR: A method to automatically produce plausible image segmentation samples from a single expert segmentation is introduced, which can have useful applications in the field of uncertainty quantification, and an illustration is provided in radiotherapy planning.
Posted Content
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Abhijit Guha Roy,Abhijit Guha Roy,Sailesh Conjeti,Debdoot Sheet,Amin Katouzian,Nassir Navab,Nassir Navab,Christian Wachinger +7 more
TL;DR: SkipDeconv-Net (SD-Net) as mentioned in this paper combines skip connections with the unpooling strategy for upsampling to address the challenges of severe class imbalance and errors along boundaries.
Journal ArticleDOI
Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region.
Jennifer P. Kieselmann,C P Kamerling,Ninon Burgos,Ninon Burgos,Martin J. Menten,Clifton D. Fuller,Simeon Nill,MJ Cardoso,MJ Cardoso,Uwe Oelfke +9 more
TL;DR: The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes, and the low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetry impact of segmentation inaccuracies on treatment planning.
Book ChapterDOI
Image Features for Brain Lesion Segmentation Using Random Forests
TL;DR: A set of hand-selected, voxel-based image features highly suitable for the tissue discrimination task and embedded in a random decision forest framework, the proposed method was applied to sub-acute ischemic stroke, acute isChemic stroke and glioma segmentation with only minor adaptation.