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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 …
A review on the use of deep learning for medical images segmentation
Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …
images. They have been used extensively for medical image segmentation as the first and …
[HTML][HTML] A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis
Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become
insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with …
insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with …
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists …
analysis. For lung segmentation in computed tomography, a variety of approaches exists …
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image
analysis methods. Given their scientific impact, it is surprising that a critical analysis of …
analysis methods. Given their scientific impact, it is surprising that a critical analysis of …
ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment,
and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from …
and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from …
Enhancement of vascular structures in 3D and 2D angiographic images
T Jerman, F Pernuš, B Likar… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
A number of imaging techniques are being used for diagnosis and treatment of vascular
pathologies like stenoses, aneurysms, embolisms, malformations and remodelings, which …
pathologies like stenoses, aneurysms, embolisms, malformations and remodelings, which …
A deep Residual U-Net convolutional neural network for automated lung segmentation in computed tomography images
To improve the early diagnosis and treatment of lung diseases automated lung
segmentation from CT images is a crucial task for clinical decision. The segmentation of the …
segmentation from CT images is a crucial task for clinical decision. The segmentation of the …
Sa-med2d-20m dataset: Segment anything in 2d medical imaging with 20 million masks
Segment Anything Model (SAM) has achieved impressive results for natural image
segmentation with input prompts such as points and bounding boxes. Its success largely …
segmentation with input prompts such as points and bounding boxes. Its success largely …
Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks
Variations in the shape and appearance of anatomical structures in medical images are
often relevant radiological signs of disease. Automatic tools can help automate parts of this …
often relevant radiological signs of disease. Automatic tools can help automate parts of this …