Curvilinear Structure Segmentation

Curvilinear structure segmentation focuses on accurately identifying and delineating line-like objects within images, a crucial task across diverse fields like medical imaging and computer vision. Recent research emphasizes developing robust deep learning models, often incorporating U-Net architectures or variations thereof, that leverage both image features and inherent geometric properties of these structures to improve segmentation accuracy and topological consistency. These advancements are driven by the need to overcome challenges posed by low contrast, thin structures, and noisy data, ultimately improving diagnostic capabilities in medicine and automating analysis in various image-based applications. Furthermore, research is actively exploring semi-supervised and weakly-supervised learning techniques to reduce the reliance on expensive, fully annotated datasets.