3D‐MASNet: 3D mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain MR images
Sign Up to like & getrecommendations! Published in 2022 at "Human Brain Mapping"
DOI: 10.1002/hbm.26174
Abstract: Precise segmentation of infant brain MR images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity… read more here.
Keywords: month old; network; brain images; brain ... See more keywords
Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory
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DOI: 10.1007/s00500-016-2210-2
Abstract: In recent decades, fuzzy segmentation methods, FCM algorithm in particular, have been widely employed for medical image segmentation, because they can save more information of the original image. But some artifacts like spatial noise and… read more here.
Keywords: based information; brain images; segmentation enhancement; information ... See more keywords
Segmentation of brain MR images using a proper combination of DCS based method with MRF
Sign Up to like & getrecommendations! Published in 2017 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-017-4696-8
Abstract: Manual segmentation of Magnetic Resonance Images (MRI) is a time-consuming process, thus automatic segmentation of brain MR images has attracted more attention in recent years. In this paper, we introduce Dynamic Classifier Selection Markov Random… read more here.
Keywords: brain images; segmentation; method; mrf ... See more keywords
Texture analysis of magnetic resonance brain images to assess multiple sclerosis lesions
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DOI: 10.1007/s11042-018-6160-9
Abstract: The novel contribution of this work is to introduce a new promising method for the analysis of textures: the Decimal Descriptor Patterns (DDP). Two best known methods of texture measures, always considered as references in… read more here.
Keywords: brain images; analysis; magnetic resonance; texture analysis ... See more keywords
Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images
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DOI: 10.1016/j.neucom.2016.05.082
Abstract: Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to… read more here.
Keywords: brain images; hippocampus; random forests; spatially localized ... See more keywords
Joint prediction of multiple scores captures better individual traits from brain images
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DOI: 10.1016/j.neuroimage.2017.06.072
Abstract: To probe individual variations in brain organization, population imaging relates features of brain images to rich descriptions of the subjects such as genetic information or behavioral and clinical assessments. Capturing common trends across these measurements… read more here.
Keywords: brain images; brain; joint prediction; prediction ... See more keywords
Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans
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DOI: 10.1109/tbme.2017.2783305
Abstract: Objective: Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF [before and after surgery, i.e., preoperative… read more here.
Keywords: brain images; based segmentation; segmentation; segmentation brain ... See more keywords
A Novel Histogram Region Merging Based Multithreshold Segmentation Algorithm for MR Brain Images
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DOI: 10.1155/2017/9759414
Abstract: Multithreshold segmentation algorithm is time-consuming, and the time complexity will increase exponentially with the increase of thresholds. In order to reduce the time complexity, a novel multithreshold segmentation algorithm is proposed in this paper. First,… read more here.
Keywords: brain images; multithreshold segmentation; segmentation algorithm; segmentation ... See more keywords
An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images
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DOI: 10.1155/2022/8342767
Abstract: Cerebellum measures taken from routinely obtained ultrasound (US) images have been frequently employed to determine gestational age and identify developing central nervous system's anatomical abnormalities. Standardized cerebellar assessments from large-scale clinical datasets are required to… read more here.
Keywords: brain images; segmentation; cerebellum segmentation; deep learning ... See more keywords
A fast stochastic framework for automatic MR brain images segmentation
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DOI: 10.1371/journal.pone.0187391
Abstract: This paper introduces a new framework for the segmentation of different brain structures (white matter, gray matter, and cerebrospinal fluid) from 3D MR brain images at different life stages. The proposed segmentation framework is based… read more here.
Keywords: brain images; order; framework; segmentation ... See more keywords