Few-shot segmentation framework for lung nodules via an optimized active contour model.

Background: Accurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longstanding challenge.

Purpose: Existing deep learning and active contour models each have their limitations.

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images.

Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion.In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation.