Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review
Objectives Medical image analysis practices face challenges that can potentially be
addressed with algorithm-based segmentation tools. In this study, we map the field of …
addressed with algorithm-based segmentation tools. In this study, we map the field of …
Automatic seeded region growing image segmentation for medical image segmentation: a brief review
N Shrivastava, J Bharti - International Journal of Image and …, 2020 - World Scientific
In the domain of computer technology, image processing strategies have become a part of
various applications. A few broadly used image segmentation methods have been …
various applications. A few broadly used image segmentation methods have been …
[HTML][HTML] Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images
Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE)
cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients …
cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients …
Validation of a regression technique for segmentation of white matter hyperintensities in Alzheimer's disease
M Dadar, TA Pascoal, S Manitsirikul… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
Segmentation and volumetric quantification of white matter hyperintensities (WMHs) is
essential in assessment and monitoring of the vascular burden in aging and Alzheimer's …
essential in assessment and monitoring of the vascular burden in aging and Alzheimer's …
[HTML][HTML] OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI
Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple
sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its …
sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its …
State-of-the-art segmentation techniques and future directions for multiple sclerosis brain lesions
Manual segmentation of multiple sclerosis (MS) in brain imaging is a challenging task due to
intra and inter-observer variability resulting in poor reproducibility. To overcome the …
intra and inter-observer variability resulting in poor reproducibility. To overcome the …
[HTML][HTML] Rotation-invariant multi-contrast non-local means for MS lesion segmentation
Multiple sclerosis (MS) lesion segmentation is crucial for evaluating disease burden,
determining disease progression and measuring the impact of new clinical treatments. MS …
determining disease progression and measuring the impact of new clinical treatments. MS …
Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease
Purpose: White matter hyperintensities (WMH) are seen on FLAIR‐MRI in several
neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and …
neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and …
A novel machine-learning framework based on a hierarchy of dispute models for the identification of fish species using multi-mode spectroscopy
M Sueker, A Daghighi, A Akhbardeh, N MacKinnon… - Sensors, 2023 - mdpi.com
Seafood mislabeling rates of approximately 20% have been reported globally. Traditional
methods for fish species identification, such as DNA analysis and polymerase chain reaction …
methods for fish species identification, such as DNA analysis and polymerase chain reaction …
Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis
I Coronado, RE Gabr… - Multiple Sclerosis …, 2021 - journals.sagepub.com
Objective: The aim of this study is to assess the performance of deep learning convolutional
neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort …
neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort …