Liu Qin, Deng Han, Lian Chunfeng, Chen Xiaoyang, Xiao Deqiang, Ma Lei, Chen Xu, Kuang Tianshu, Gateno Jaime, Yap Pew-Thian, Xia James J
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, USA.
Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital, TX, USA.
Mach Learn Med Imaging. 2021 Sep;12966:606-614. doi: 10.1007/978-3-030-87589-3_62. Epub 2021 Sep 21.
Accurate bone segmentation and landmark detection are two essential preparation tasks in computer-aided surgical planning for patients with craniomaxillofacial (CMF) deformities. Surgeons typically have to complete the two tasks manually, spending ~12 hours for each set of CBCT or ~5 hours for CT. To tackle these problems, we propose a multi-stage coarse-to-fine CNN-based framework, called SkullEngine, for high-resolution segmentation and large-scale landmark detection through a collaborative, integrated, and scalable JSD model and three segmentation and landmark detection refinement models. We evaluated our framework on a clinical dataset consisting of 170 CBCT/CT images for the task of segmenting 2 bones (midface and mandible) and detecting 175 clinically common landmarks on bones, teeth, and soft tissues. Experimental results show that SkullEngine significantly improves segmentation quality, especially in regions where the bone is thin. In addition, SkullEngine also efficiently and accurately detect all of the 175 landmarks. Both tasks were completed simultaneously within 3 minutes regardless of CBCT or CT with high segmentation quality. Currently, SkullEngine has been integrated into a clinical workflow to further evaluate its clinical efficiency.
准确的骨分割和地标检测是颅颌面（CMF）畸形患者计算机辅助手术规划中的两项基本准备任务。外科医生通常必须手动完成这两项任务，每组CBCT需要花费约12小时，CT则需要约5小时。为了解决这些问题，我们提出了一个基于卷积神经网络（CNN）的多阶段粗到细框架，称为SkullEngine，通过一个协作、集成且可扩展的联合相似性散度（JSD）模型以及三个分割和地标检测细化模型，用于高分辨率分割和大规模地标检测。我们在一个由170张CBCT/CT图像组成的临床数据集上评估了我们的框架，该数据集用于分割两块骨头（中面部和下颌骨）以及检测骨头、牙齿和软组织上的175个临床常见地标。实验结果表明，SkullEngine显著提高了分割质量，尤其是在骨头较薄的区域。此外，SkullEngine还能高效且准确地检测出所有175个地标。无论使用CBCT还是CT，两项任务都能在3分钟内同时完成，且分割质量很高。目前，SkullEngine已被集成到临床工作流程中，以进一步评估其临床效率。
