Hierarchical, learning-based automatic liver segmentation

In this paper we present a hierarchical, learning-based approach for automatic and accurate liver segmentation from 3D CT volumes. We target CT volumes that come from largely diverse sources (e.g., diseased in six different organs) and are generated by different scanning protocols (e.g., contrast and non-contrast, various resolution and position). Three key ingredients are combined to solve the segmentation problem. First, a hierarchical framework is used to efficiently and effectively monitor the accuracy propagation in a coarse-to-fine fashion. Second, two new learning techniques, marginal space learning and steerable features, are applied for robust boundary inference. This enables handling of highly heterogeneous texture pattern. Third, a novel shape space initialization is proposed to improve traditional methods that are limited to similarity transformation. The proposed approach is tested on a challenging dataset containing 174 volumes. Our approach not only produces excellent s...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Haibin Ling, Shaohua Kevin Zhou, Yefeng Zheng, Bogdan Georgescu, Michael Sühling, Dorin Comaniciu
• Keywords: Accurate Liver Segmentation, Computer Vision, Ct Volumes, CVPR 2008, Marginal Space Learning, Segmentation Problem, Shape Space Initialization