Liver Segmentation in Abdominal CT Images by Adaptive 3D Region Growing
This work addresses the problem of automating liver segmentation for computer-aided diagnosis, reducing manual effort and errors, but it appears incremental as it builds on prior methods like Deeds registration.
The paper tackled liver segmentation in abdominal CT images by proposing an adaptive 3D region growing method with subject-specific conditions, achieving a Dice score of 92.56%.
Automatic liver segmentation plays an important role in computer-aided diagnosis and treatment. Manual segmentation of organs is a difficult and tedious task and so prone to human errors. In this paper, we propose an adaptive 3D region growing with subject-specific conditions. For this aim we use the intensity distribution of most probable voxels in prior map along with location prior. We also incorporate the boundary of target organs to restrict the region growing. In order to obtain strong edges and high contrast, we propose an effective contrast enhancement algorithm to facilitate more accurate segmentation. In this paper, 92.56% Dice score is achieved. We compare our method with the method of hard thresholding on Deeds prior map and also with the majority voting on Deeds registration with 13 organs.