CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging
Automated detection of curvilinear structures, eg, blood vessels or nerve fibres, from medical
and biomedical images is a crucial early step in automatic image interpretation associated to …
and biomedical images is a crucial early step in automatic image interpretation associated to …
Joint topology-preserving and feature-refinement network for curvilinear structure segmentation
Curvilinear structure segmentation (CSS) is under semantic segmentation, whose
applications include crack detection, aerial road extraction, and biomedical image …
applications include crack detection, aerial road extraction, and biomedical image …
Local intensity order transformation for robust curvilinear object segmentation
Segmentation of curvilinear structures is important in many applications, such as retinal
blood vessel segmentation for early detection of vessel diseases and pavement crack …
blood vessel segmentation for early detection of vessel diseases and pavement crack …
FreeCOS: self-supervised learning from fractals and unlabeled images for curvilinear object segmentation
Curvilinear object segmentation is critical for many applications. However, manually
annotating curvilinear objects is very time-consuming and error-prone, yielding insufficiently …
annotating curvilinear objects is very time-consuming and error-prone, yielding insufficiently …
How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison
U Sehar, ML Naseem - Multimedia Tools and Applications, 2022 - Springer
Semantic segmentation involves extracting meaningful information from images or input
from a video or recording frames. It is the way to perform the extraction by checking pixels by …
from a video or recording frames. It is the way to perform the extraction by checking pixels by …
CSGNet: Cascade semantic guided net for retinal vessel segmentation
S Guo - Biomedical Signal Processing and Control, 2022 - Elsevier
Retinal vessels are essential biomarkers for the early diagnosis of ophthalmic diseases.
Accurate segmentation of retinal vessels is necessary for further quantitative analysis of …
Accurate segmentation of retinal vessels is necessary for further quantitative analysis of …
Mind Marginal Non-Crack Regions: Clustering-Inspired Representation Learning for Crack Segmentation
Crack segmentation datasets make great efforts to obtain the ground truth crack or non-crack
labels as clearly as possible. However it can be observed that ambiguities are still inevitable …
labels as clearly as possible. However it can be observed that ambiguities are still inevitable …
CED-Net: context-aware ear detection network for unconstrained images
Personal authentication systems based on biometric have seen a strong demand mainly
due to the increasing concern in various privacy and security applications. Although the use …
due to the increasing concern in various privacy and security applications. Although the use …
Icbnet: Iterative context-boundary feedback network for polyp segmentation
Accurate polyp segmentation from colonoscopy images, which is critical to automatic
colorectal cancer diagnosis, attracts increasing attentions in recent years. Most existing …
colorectal cancer diagnosis, attracts increasing attentions in recent years. Most existing …
Skeleton recall loss for connectivity conserving and resource efficient segmentation of thin tubular structures
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete
cracks, is a crucial task in computer vision. Standard deep learning-based segmentation …
cracks, is a crucial task in computer vision. Standard deep learning-based segmentation …