Luo Yanjing, Kouchakinezhad Mohammadtaha, Repp Felix, Scheper Verena, Lenarz Thomas, Matin-Mann Farnaz
Department of Otorhinolaryngology, Head and Neck Surgery, Hannover Medical School, 30625 Hannover, Germany.
Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), Hannover Medical School, 30625 Hannover, Germany.
J Imaging. 2025 Aug 8;11(8):264. doi: 10.3390/jimaging11080264.
External ear canal (EEC) stenosis, often associated with cholesteatoma, carries a high risk of postoperative restenosis despite surgical intervention. While individualized implants offer promise in preventing restenosis, the high morphological variability of EECs and the lack of standardized definitions hinder systematic implant design. This study aimed to characterize individual EEC morphology and to develop a validated automated segmentation system for efficient implant preparation. Reference datasets were first generated by manual segmentation using 3D Slicer software version 5.2.2. Based on these, we developed a customized plugin capable of automatically identifying the maximal implantable region within the EEC and measuring its key dimensions. The accuracy of the plugin was assessed by comparing it with manual segmentation results in terms of shape, volume, length, and width. Validation was further performed using three temporal bone implantation experiments with 3D-Bioplotter©-fabricated EEC implants. The automated system demonstrated strong consistency with manual methods and significantly improved segmentation efficiency. The plugin-generated models enabled successful implant fabrication and placement in all validation tests. These results confirm the system's clinical feasibility and support its use for individualized and systematic EEC implant design. The developed tool holds potential to improve surgical planning and reduce postoperative restenosis in EEC stenosis treatment.
外耳道（EEC）狭窄通常与胆脂瘤相关，尽管进行了手术干预，但术后再狭窄的风险仍然很高。虽然个性化植入物有望预防再狭窄，但EEC的高度形态变异性和缺乏标准化定义阻碍了系统的植入物设计。本研究旨在表征个体EEC形态，并开发一种经过验证的自动分割系统，以高效制备植入物。首先使用3D Slicer软件5.2.2版本通过手动分割生成参考数据集。在此基础上，我们开发了一个定制插件，能够自动识别EEC内的最大可植入区域并测量其关键尺寸。通过将该插件与手动分割结果在形状、体积、长度和宽度方面进行比较，评估了该插件的准确性。使用3D-Bioplotter©制造的EEC植入物进行的三个颞骨植入实验进一步进行了验证。该自动系统与手动方法表现出很强的一致性，并显著提高了分割效率。插件生成的模型在所有验证测试中都能成功制造和放置植入物。这些结果证实了该系统的临床可行性，并支持将其用于个性化和系统的EEC植入物设计。所开发的工具具有改善手术规划和减少EEC狭窄治疗术后再狭窄的潜力。
