Curvilinear structure segmentation (CSS) is under semantic segmentation, whose
applications include crack detection, aerial road extraction, and biomedical image …

Context is essential for semantic segmentation. Due to the diverse shapes of objects and
their complex layout in various scene images, the spatial scales and shapes of contexts for …

Curvilinear structure segmentation is important in medical imaging, quantifying structures
such as vessels, airways, neurons, or organ boundaries in 2D slices. Segmentation via pixel …

Current state-of-the-art methods for image segmentation form a dense image representation
where the color, shape and texture information are all processed together inside a deep …

In this work, we address the challenging issue of scene segmentation. To increase the
feature similarity of the same object while keeping the feature discrimination of different …

Pre-training is a strong strategy for enhancing visual models to efficiently train them with a
limited number of labeled images. In semantic segmentation, creating annotation masks …

Humans are able to recognize structured relations in observation, allowing us to decompose
complex scenes into simpler parts and abstract the visual world in multiple levels. However …

The detection of curvilinear structures in medical images, eg, blood vessels or nerve fibers,
is important in aiding management of many diseases. In this work, we propose a general …

In this work, we propose to address the existing problem of biomedical image segmentation
that often produces results, which fail to capture the exact contours of the target and suffer …

Accurate segmentation of topological tubular structures, such as blood vessels and roads, is
crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However …