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Multi-Stage Liver Segmentation in CT Scans Using Gaussian Pseudo Variance Level Set
No single technology can be rich enough to segment accurately due to the challenges of liver segmentation, which include low contrast with neighboring organs and the presence of pathology as well as highly varied shapes between subjects. This paper presents a Multi-stage framework for location and segmentation. First, Faster RCNN is employed to locate the liver region. Then, the Gaussian mixture model-based signed distance function is proposed to increase the flexibility of shape prior models.doi:10.1109/access.2021.3097387 fatcat:227bycuhpzhr7dybezs3jbkyq4