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
The ImageJ ecosystem: An open platform for biomedical image analysis
TL;DR: The ImageJ project is used as a case study of how open‐source software fosters its suites of software tools, making multitudes of image‐analysis technology easily accessible to the scientific community.
Journal ArticleDOI
Brain development in children and adolescents: insights from anatomical magnetic resonance imaging.
Rhoshel K. Lenroot,Jay N. Giedd +1 more
TL;DR: Key findings related to brain anatomical changes during childhood and adolescent are increases in white matter volumes throughout the brain and regionally specific inverted U-shaped trajectories of gray matter volumes.
Journal ArticleDOI
Artificial intelligence in radiology
Ahmed Hosny,Chintan Parmar,John Quackenbush,Lawrence H. Schwartz,Lawrence H. Schwartz,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +6 more
TL;DR: A general understanding of AI methods, particularly those pertaining to image-based tasks, is established and how these methods could impact multiple facets of radiology is explored, with a general focus on applications in oncology.
Journal ArticleDOI
Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure
Songcan Chen,Daoqiang Zhang +1 more
TL;DR: Two variants of fuzzy c-means clustering with spatial constraints, using the kernel methods, are proposed, inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering theNon-E Euclidean structures in data.
Journal ArticleDOI
MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation.
Nabil Ibtehaz,M. Sohel Rahman +1 more
TL;DR: This work develops a novel architecture, MultiResUNet, as the potential successor to the U-Net architecture, and tests and compared it with the classical U- net on a vast repertoire of multimodal medical images.
References
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Comparison of supervised MRI segmentation methods for tumor volume determination during therapy
M. Vaidyanathan,Laurence P. Clarke,R.P. Velthuizen,S. Phuphanich,A. Bensaid,Lawrence O. Hall,James C. Bezdek,Harvey Greenberg,A. Trotti,Martin S. Silbiger +9 more
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Dynamic programming generation of curves on brain surfaces
TL;DR: Dynamic programming algorithms are presented for automated generation of length minimizing geodesics and curves of extremal curvature on the neocortex of the macaque and the visible human.
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Neuroanatomical segmentation in MRI : Technological objectives
TL;DR: A definition of precise, comprehensive, robust and practical neuroanatomical segmentation in magnetic resonance brain images with the goal of performing quantitative morphometric analyses is offered.
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Neural network methods for volumetric magnetic resonance imaging of the human brain
TL;DR: The clinical and research needs for brain imaging are surveyed, the state-of-the-art in relevant image analysis techniques are presented, and the use of novel artificial neural networks which have a recurrent structure to extract precise morphometric information from MRI scans of the human brain are discussed.
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Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions
TL;DR: Modifications to the basic 2D split-and-merge method, based on the principles of simulated annealing and controlled boundary elimination, are developed and discussed and the properties of the 3D approach are demonstrated by the automatic quantitation of brain ventricle volume, producing estimates to within 7% of validated manual methods.