MRI segmentation of the human brain: challenges, methods, and applications
I Despotović, B Goossens… - … mathematical methods in …, 2015 - Wiley Online Library
Image segmentation is one of the most important tasks in medical image analysis and is
often the first and the most critical step in many clinical applications. In brain MRI analysis …
often the first and the most critical step in many clinical applications. In brain MRI analysis …
Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation
Abstract In recent years Deep Learning has brought about a breakthrough in Medical Image
Segmentation. In this regard, U-Net has been the most popular architecture in the medical …
Segmentation. In this regard, U-Net has been the most popular architecture in the medical …
Modified grasshopper algorithm-based multilevel thresholding for color image segmentation
Multilevel thresholding is an important approach for image segmentation which has drawn
much attention during the past few years. The Tsallis entropy method is implemented for its …
much attention during the past few years. The Tsallis entropy method is implemented for its …
RMAU-Net: residual multi-scale attention U-Net for liver and tumor segmentation in CT images
L Jiang, J Ou, R Liu, Y Zou, T Xie, H Xiao… - Computers in Biology and …, 2023 - Elsevier
Liver cancer is one of the leading causes of cancer-related deaths worldwide. Automatic
liver and tumor segmentation are of great value in clinical practice as they can reduce …
liver and tumor segmentation are of great value in clinical practice as they can reduce …
MRI brain tumor segmentation based on texture features and kernel sparse coding
An automatic brain tumor segmentation method based on texture feature and kernel sparse
coding from FLAIR (fluid attenuated inversion recovery) contrast-enhanced MRIs (magnetic …
coding from FLAIR (fluid attenuated inversion recovery) contrast-enhanced MRIs (magnetic …
Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images
PP Rebouças Filho, PC Cortez, AC da Silva Barros… - Medical image …, 2017 - Elsevier
Abstract The World Health Organization estimates that 300 million people have asthma, 210
million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to …
million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to …
State-of-the-art methods for brain tissue segmentation: A review
Brain tissue segmentation is one of the most sought after research areas in medical image
processing. It provides detailed quantitative brain analysis for accurate disease diagnosis …
processing. It provides detailed quantitative brain analysis for accurate disease diagnosis …
A review of automated methods for the detection of sickle cell disease
Detection of sickle cell disease is a crucial job in medical image analysis. It emphasizes
elaborate analysis of proper disease diagnosis after accurate detection followed by a …
elaborate analysis of proper disease diagnosis after accurate detection followed by a …
Survey on brain tumor segmentation and feature extraction of MR images
S Saman, S Jamjala Narayanan - International journal of multimedia …, 2019 - Springer
Brain tumor analysis plays an important role in medical imaging applications and in
delivering a huge amount of anatomical and functional information, which increases and …
delivering a huge amount of anatomical and functional information, which increases and …