Journal ArticleDOI
Current methods in medical image segmentation.
TLDR
A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented, with an emphasis on the advantages and disadvantages of these methods for medical imaging applications.Abstract:
▪ Abstract Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. We present a critical appraisal of the current status of semiautomated and automated methods for the segmentation of anatomical medical images. Terminology and important issues in image segmentation are first presented. Current segmentation approaches are then reviewed with an emphasis on the advantages and disadvantages of these methods for medical imaging applications. We conclude with a discussion on the future of image segmentation methods in biomedical research.read more
Citations
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Journal ArticleDOI
Performance measure characterization for evaluating neuroimage segmentation algorithms.
TL;DR: It is indicated that Conformity is more sensitive and rigorous than Jaccard and Dice in that it has better discrimination capabilities in detecting small variations in segmented images.
Journal ArticleDOI
Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms.
TL;DR: This paper reviews recent advances in these three areas applied to shape, growth, and atrophy within anatomical models.
Journal ArticleDOI
Digitizing life at the level of the cell: high-performance laser-scanning microscopy and image analysis for in toto imaging of development
Sean G. Megason,Scott E. Fraser +1 more
TL;DR: The current state-of-the-art, the challenges, and the prospects of in toto imaging in the areas of imaging, image analysis, and informatics are discussed.
Journal ArticleDOI
Survey on deep learning for radiotherapy.
TL;DR: The concept of deep learning is explained, addressing it in the broader context of machine learning, and the most common network architectures are presented, with a more specific focus on convolutional neural networks.
Journal ArticleDOI
Evaluation of automated brain MR image segmentation and volumetry methods.
Frederick Klauschen,Aaron L. Goldman,Vincent Barra,Andreas Meyer-Lindenberg,Arvid Lundervold +4 more
TL;DR: Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies.
References
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TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
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TL;DR: Direct and Indirect Radiologic Localization Reference System: Basal Brain Line CA-CP Cerebral Structures in Three-Dimensional Space Practical Examples for the Use of the Atlas in Neuroradiologic Examinations Three- Dimensional Atlas of a Human Brain Nomenclature-Abbreviations Anatomic Index Conclusions.