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.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Abstract Purpose:To evaluate the dosimetric accuracy of using deformed CT images for dose calculations, and to assess the feasibility of using these images during adaptive radiotherapy, for nasopharyngeal carcinoma (NPC) patients. Methods and Materials:Thirty consecutive NPC patients who had undergone one re-plan in their radiotherapy treatments were selected. The pre-treatment planning computed (82%) dosimetric endpoints of the OARs also showed statistically significant dose differences (P<0.05). Conclusions:Dose calculations using deformed PCT images could result in significant dose uncertainties for NPC patients. Further investigations are needed; meanwhile, it is suggested that a new PCT should be acquired in the case of re-planning at mid-course.

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