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 …
Multi-atlas segmentation of biomedical images: a survey
JE Iglesias, MR Sabuncu - Medical image analysis, 2015 - Elsevier
Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images
History shows that the infectious disease (COVID-19) can stun the world quickly, causing
massive losses to health, resulting in a profound impact on the lives of billions of people …
massive losses to health, resulting in a profound impact on the lives of billions of people …
Deepcut: Object segmentation from bounding box annotations using convolutional neural networks
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations
given an image dataset labelled weak annotations, in our case bounding boxes. It extends …
given an image dataset labelled weak annotations, in our case bounding boxes. It extends …
Current methods in medical image segmentation
▪ 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 …
automating or facilitating the delineation of anatomical structures and other regions of …
Methods on skull stripping of MRI head scan images—a review
P Kalavathi, VBS Prasath - Journal of digital imaging, 2016 - Springer
The high resolution magnetic resonance (MR) brain images contain some non-brain tissues
such as skin, fat, muscle, neck, and eye balls compared to the functional images namely …
such as skin, fat, muscle, neck, and eye balls compared to the functional images namely …
[HTML][HTML] Automated medical image segmentation techniques
N Sharma, LM Aggarwal - Journal of medical physics, 2010 - journals.lww.com
Accurate segmentation of medical images is a key step in contouring during radiotherapy
planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most …
planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most …
BEaST: brain extraction based on nonlocal segmentation technique
Brain extraction is an important step in the analysis of brain images. The variability in brain
morphology and the difference in intensity characteristics due to imaging sequences make …
morphology and the difference in intensity characteristics due to imaging sequences make …
BrainSuite: an automated cortical surface identification tool
DW Shattuck, RM Leahy - Medical image analysis, 2002 - Elsevier
We describe a new magnetic resonance (MR) image analysis tool that produces cortical
surface representations with spherical topology from MR images of the human brain. The …
surface representations with spherical topology from MR images of the human brain. The …
Magnetic resonance image tissue classification using a partial volume model
DW Shattuck, SR Sandor-Leahy, KA Schaper… - NeuroImage, 2001 - Elsevier
We describe a sequence of low-level operations to isolate and classify brain tissue within T1-
weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue …
weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue …