Blood vessel segmentation algorithms—review of methods, datasets and evaluation metrics

S Moccia, E De Momi, S El Hadji, LS Mattos - Computer methods and …, 2018 - Elsevier
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 …

All answers are in the images: A review of deep learning for cerebrovascular segmentation

C Chen, K Zhou, Z Wang, Q Zhang, R **ao - Computerized Medical Imaging …, 2023 - Elsevier
Cerebrovascular imaging is a common examination. Its accurate cerebrovascular
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

A Zeng, C Wu, G Lin, W **e, J Hong, M Huang… - … Medical Imaging and …, 2023 - Elsevier
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 …

Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier

JM Wolterink, RW van Hamersvelt, MA Viergever… - Medical image …, 2019 - Elsevier
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 …

Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer

JS Bredfeldt, Y Liu, CA Pehlke… - … of biomedical optics, 2014 - spiedigitallibrary.org
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 …

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 …

Attention-guided deep neural network with multi-scale feature fusion for liver vessel segmentation

Q Yan, B Wang, W Zhang, C Luo, W Xu… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
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 …

A novel multi-attention, multi-scale 3D deep network for coronary artery segmentation

C Dong, S Xu, D Dai, Y Zhang, C Zhang, Z Li - Medical Image Analysis, 2023 - Elsevier
Automatic segmentation of coronary arteries provides vital assistance to enable accurate
and efficient diagnosis and evaluation of coronary artery disease (CAD). However, the task …

[CARTE][B] Visual computing for medicine: theory, algorithms, and applications

B Preim, CP Botha - 2013 - books.google.com
Visual Computing for Medicine, Second Edition, offers cutting-edge visualization techniques
and their applications in medical diagnosis, education, and treatment. The book includes …

Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography

G Yang, P Kitslaar, M Frenay, A Broersen… - The international journal …, 2012 - Springer
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 …