Radiomics: a primer on high-throughput image phenotyping
Radiomics is a high-throughput approach to image phenotyping. It uses computer
algorithms to extract and analyze a large number of quantitative features from radiological …
algorithms to extract and analyze a large number of quantitative features from radiological …
Computational methods for liver vessel segmentation in medical imaging: A review
M Ciecholewski, M Kassjański - Sensors, 2021 - mdpi.com
The segmentation of liver blood vessels is of major importance as it is essential for
formulating diagnoses, planning and delivering treatments, as well as evaluating the results …
formulating diagnoses, planning and delivering treatments, as well as evaluating the results …
DV-Net: Accurate liver vessel segmentation via dense connection model with D-BCE loss function
Recently, liver vessel segmentation has aroused widespread interest in medical image
analysis. Accurately extracting blood vessels from livers is a difficult task due to their …
analysis. Accurately extracting blood vessels from livers is a difficult task due to their …
Fully automatic liver and tumor segmentation from CT image using an AIM-Unet
The segmentation of the liver is a difficult process due to the changes in shape, border, and
density that occur in each section in computed tomography (CT) images. In this study, the …
density that occur in each section in computed tomography (CT) images. In this study, the …
UAVs joint vehicles as data mules for fast codes dissemination for edge networking in smart city
With the rapid development of software-defined technologies, emerging multimedia
applications are booming, which require real-time communication and computation via …
applications are booming, which require real-time communication and computation via …
Semi-automatic liver tumor segmentation with adaptive region growing and graph cuts
Z Yang, Y Zhao, M Liao, S Di, Y Zeng - Biomedical signal processing and …, 2021 - Elsevier
Segmenting liver tumors from computed tomography (CT) images plays a very important role
in computer-aided diagnosis, surgical planning, and treatment monitoring. However …
in computer-aided diagnosis, surgical planning, and treatment monitoring. However …
Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans
Segmentation of liver and hepatic lesions using computed tomography (CT) is a critical and
challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of …
challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of …
Hepatic vessels segmentation using deep learning and preprocessing enhancement
Purpose Liver hepatic vessels segmentation is a crucial step for the diagnosis process in
patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal …
patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal …
Generative adversarial network based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image
The accurate segmentation of cerebral vessels from time-of-flight magnetic resonance
angiography (TOF-MRA) data is crucial for the diagnosis and treatment of cerebrovascular …
angiography (TOF-MRA) data is crucial for the diagnosis and treatment of cerebrovascular …
A characteristic function-based algorithm for geodesic active contours
Active contour models have been widely used in image segmentation, and the level set
method (LSM) is the most popular approach for solving the models, via implicitly …
method (LSM) is the most popular approach for solving the models, via implicitly …