(2006) Automated Hepatic Volumetry for Living Related Liver Transplantation at Multisection CT 1. Nakayama, Y., Li, Q., Katsuragawa, S., Ikeda, R., Hiai, Y., Awai, K., Kusunoki, S., Yamashita, Y., Okajima, H., Inomata, Y., et al. Meanwhile, the results of liver segmentation only using edge images presented 79.17% ± 5.15% or statistical regions showed 74.04% ± 9.77% of similarities.
#Consequent boundary manual#
The segmentation results showed 86.38% ± 4.26% (DSC: 91.38% ± 2.99%) of similarities to outlines of manual delineation provided by a radiologist. We applied the proposed system to 40 sets of 3D CT-liver data, which were acquired from four patients (10 different sets per patient) by a 4D-CT imaging system. Lastly, the computation time in a level-set based on reaction-diffusion evolution and the GAC method is reduced by using a concept of multi-resolution. These images help a geodesic active contour (GAC) to move without obstruction from high level of image noises. Second, we introduce liver-corrective images to represent statistical regions of the liver and preserve edge information.
The seed regions are allocated inside the liver to measure statistical values of its gray-intensities. The mask regions assist in prevention of leakage regions due to an overlap of gray-intensities between liver and another soft-tissue around ribs and verte-brae. First, an effective initial process creates mask and seed regions. This paper presents an efficient liver-segmentation system developed by combining three ideas under the operations of a level-set method and consequent processes.
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