Efficient interactive 3D Livewire segmentation of complex objects with arbitrary topology

We present a novel interactive method based on a 3D Livewire approach for segmenting complex objects of arbitrary topologies. Our proposed technique automatically and seamlessly handles objects with branchings, concavities, protrusions, and non-spherical topologies with minimal user-input. Given sparse interactively segmented contours on orthogonal slices, our proposed method determines Livewire seedpoints on all slices in the third orthogonal direction, which are used to mimic user-guided segmentation. In doing so, our method pre-processes these points to increase algorithm robustness, and uses a novel seedpoint sorting method using ideas from L-system's Turtle algorithm. Moreover, we present a segmentation tool based on our proposed framework and demonstrate the robustness of our approach on real medical data. Results highlight the superior performance of the proposed method with validation tests on synthetic and real MRI and CT data, with segmentation reproducibility exceeding 95% and segmentation task time decreasing to less than 20% when compared to performing 2D Livewire on each volume slice.