Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However - due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut (GC) was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score (DSC) and the Hausdorff distance (HD). Overall segmentation times were about one minute. Mean DSC values of over 85% and HD below 33.5 voxel could be achieved. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94). Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works.

中文翻译：

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下颌骨半自动开源算法软件分割的临床评估：新行动方案的实际可行性和评估

基于算法软件分割的计算机辅助技术是复杂手术病例中越来越受关注的话题。然而，由于功能不稳定、耗时的软件流程、人力资源或基于许可的财务成本，许多细分流程通常从临床中心外包给第三方和行业。因此，该试验的目的是评估在临床实践中使用的易于获得、功能稳定且无需许可的分割方法的实际可行性。在这个回顾性的、随机的、受控的跟踪中，通过使用来自临床的 10 个 CT 下颌数据集与相同解剖结构的手动生成的地面实况进行比较，评估了基于开源的分割算法 GrowCut (GC) 的准确性和一致性。常规。评估参数是分割时间、体积、体素数、骰子分数 (DSC) 和豪斯多夫距离 (HD)。整体分段时间约为一分钟。可以实现超过 85% 的平均 DSC 值和低于 33.5 体素的 HD。评估参数之间的统计差异不显着（p<0.05），相关系数接近值一（r > 0.94）。通过所提出的基于交互式开源的方法，可以执行具有高精度和高正相关性的完整功能稳定且省时的分割。在颅颌面复合体中，所使用的方法可以代表临床实践中基于图像分割的算法替代方案，例如手术治疗计划或术后结果的可视化，并提供几个优点。