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Blood vessel segmentation algorithms—review of methods, datasets and evaluation metrics
Background Blood vessel segmentation is a topic of high interest in medical image analysis
since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and …
since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and …
All answers are in the images: A review of deep learning for cerebrovascular segmentation
Cerebrovascular imaging is a common examination. Its accurate cerebrovascular
segmentation become an important auxiliary method for the diagnosis and treatment of …
segmentation become an important auxiliary method for the diagnosis and treatment of …
ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images
Cardiovascular disease (CVD) accounts for about half of non-communicable diseases.
Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed …
Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed …
Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a
prerequisite for evaluation of stenoses and atherosclerotic plaque. In this work, we propose …
prerequisite for evaluation of stenoses and atherosclerotic plaque. In this work, we propose …
Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer
Second-harmonic generation (SHG) imaging can help reveal interactions between collagen
fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged …
fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged …
Robust liver vessel extraction using 3D U-Net with variant dice loss function
Q Huang, J Sun, H Ding, X Wang, G Wang - Computers in biology and …, 2018 - Elsevier
Purpose Liver vessel extraction from CT images is essential in liver surgical planning. Liver
vessel segmentation is difficult due to the complex vessel structures, and even expert …
vessel segmentation is difficult due to the complex vessel structures, and even expert …
Attention-guided deep neural network with multi-scale feature fusion for liver vessel segmentation
Liver vessel segmentation is fast becoming a key instrument in the diagnosis and surgical
planning of liver diseases. In clinical practice, liver vessels are normally manual annotated …
planning of liver diseases. In clinical practice, liver vessels are normally manual annotated …
A novel multi-attention, multi-scale 3D deep network for coronary artery segmentation
Automatic segmentation of coronary arteries provides vital assistance to enable accurate
and efficient diagnosis and evaluation of coronary artery disease (CAD). However, the task …
and efficient diagnosis and evaluation of coronary artery disease (CAD). However, the task …
[CARTE][B] Visual computing for medicine: theory, algorithms, and applications
Visual Computing for Medicine, Second Edition, offers cutting-edge visualization techniques
and their applications in medical diagnosis, education, and treatment. The book includes …
and their applications in medical diagnosis, education, and treatment. The book includes …
Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography
Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality
for the visualization of the heart and coronary arteries. To fully exploit the potential of the …
for the visualization of the heart and coronary arteries. To fully exploit the potential of the …