Active shape models—their training and application
Computer Vision and Image Understanding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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Heart isolation (separating the heart from the proximity tissues, e.g., lung, liver, and rib cage) is a prerequisite to clearly visualize the coronary arteries in 3D. Such a 3D visualization provides an intuitive view to physicians to diagnose suspicious coronary segments. Heart isolation is also necessary in radiotherapy planning to mask out the heart for the treatment of lung or liver tumors. In this paper, we propose an efficient and robust method for heart isolation in computed tomography (CT) volumes. Marginal space learning (MSL) is used to efficiently estimate the position, orientation, and scale of the heart. An optimal mean shape (which optimally represents the whole shape population) is then aligned with detected pose, followed by boundary refinement using a learning-based boundary detector. Post-processing is further exploited to exclude the rib cage from the heart mask. A large-scale experiment on 589 volumes (including both contrasted and non-contrasted scans) from 288 patients demonstrates the robustness of the approach. It achieves a mean point-to-mesh error of 1.91 mm. Running at a speed of 1.5 s/volume, it is at least 10 times faster than the previous methods.