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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Bayesian logistic shape model inference: Application to cochlear image segmentation.
Zihao Wang,Dr. Nuzhath Parven,Thomas Demarcy,Clair Vandersteen,Dan Gnansia,Charles Raffaelli,Nicolas Guevara,Hervé Delingette +7 more
TL;DR: In this article, a Bayesian inference of parametric shape models for segmenting medical images with the objective of providing interpretable results is proposed. But, this work is limited to the segmentation of cochlear structures from clinical CT images constrained by a 10-parameter shape model.
Journal ArticleDOI
Segmentation evaluation with sparse ground truth data: Simulating true segmentations as perfect/imperfect as those generated by humans
TL;DR: In this article, the authors proposed a method, named SparseGT, which exploit variability among human segmenters to maximally save manual workload in ground truth segmentation for evaluating actual segmentations by algorithms.
Journal ArticleDOI
Bayesian logistic shape model inference: Application to cochlear image segmentation
TL;DR: In this paper , a Bayesian inference of parametric shape models for segmenting medical images with the objective of providing interpretable results is proposed. But, this work is limited to the segmentation of cochlear structures from clinical CT images constrained by a 10-parameter shape model.
Journal ArticleDOI
Development and evaluation of an automated EPTN-consensus based organ at risk atlas in the brain on MRI.
J. Crouzen,A. Petoukhova,Ruud Wiggenraad,S. Hutschemaekers,Christa G Gadellaa-van Hooijdonk,Noëlle C. van der Voort van Zyp,Mirjam E. Mast,Jaap D. Zindler +7 more
TL;DR: In this paper , the authors developed an MR-based OAR autosegmentation atlas and evaluated its performance compared to manual delineation using the Dice similarity coefficient (DSC).
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A novel MCF-Net: Multi-level context fusion network for 2D medical image segmentation
TL;DR: Wang et al. as discussed by the authors proposed a multi-level context fusion network (MCF-Net) to improve the performance of U-Net on various segmentation tasks by designing three modules, hybrid attention-based residual atrous convolution (HARA), multi-scale feature memory (MSFM), and multi-receptive field fusion (MRFF) module, to fuse multilevel contextual information.
References
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Maximum likelihood from incomplete data via the EM algorithm
Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
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Machine Learning : A Probabilistic Perspective
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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On the limited memory BFGS method for large scale optimization
Dong C. Liu,Jorge Nocedal +1 more
TL;DR: The numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and is better able to use additional storage to accelerate convergence, and the convergence properties are studied to prove global convergence on uniformly convex problems.
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Training products of experts by minimizing contrastive divergence
TL;DR: A product of experts (PoE) is an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary because it is hard even to approximate the derivatives of the renormalization term in the combination rule.