A review of 3D vessel lumen segmentation techniques.
For instance, 1D centerlines can first be obtained from minimal paths and serve to initialize active contours for an accurate segmentation of the vessels’ surface (see Fig. 11). 5. Discussion. The medical interest in 3D vascular segmentation and the challenges it raises have motivated a tremendous amount of research work.This multi-step segmentation procedure consists of four steps: brain tissue segmentation (1), fuzzy-based vessel enhancement (2), extraction of cerebrovascular structures using a level-set method with anisotropic energy weights (3), and detection and correction of gaps in the vessel segmentation (4).The Vascular Modeling Toolkit. Supported by Orobix srl. vmtk is a collection of libraries and tools for 3D reconstruction, geometric analysis, mesh generation and surface data analysis for image-based modeling of blood vessels.
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning. 23 Nov 2019. We here propose to model the dynamic process of iterative interactive image segmentation as a Markov decision process (MDP) and solve it with reinforcement learning (RL).GeoS - free tool for semi-automatic segmentation of 3D medical scans (Antonio Criminisi, Microsoft) ITK-SNAP: a software application used to segment structures in 3D medical images; Live-Vessel - interactive, Live-Wire-like segmentation tool for tubular structures (e.g. vessels).
A review: Deep learning for medical image segmentation using multi-modality fusion. 22 Apr 2020. Due to their self-learning and generalization ability over large amounts of data, deep learning recently has also gained great interest in multi-modal medical image segmentation.
