@inproceedings{a2748449ce8e491a9e577d265a6497e1,
title = "SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection",
abstract = "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 h for each set of CBCT or ∼ 5 h 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 min regardless of CBCT or CT with high segmentation quality. Currently, SkullEngine has been integrated into a clinical workflow to further evaluate its clinical efficiency.",
keywords = "Cone-Beam Computed Tomography (CBCT) Image, Landmark Detection, Segmentation",
author = "Qin Liu and Han Deng and Chunfeng Lian and Xiaoyang Chen and Deqiang Xiao and Lei Ma and Xu Chen and Tianshu Kuang and Jaime Gateno and Yap, {Pew Thian} and Xia, {James J.}",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 27-09-2021",
year = "2021",
doi = "10.1007/978-3-030-87589-3_62",
language = "English",
isbn = "9783030875886",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "606--614",
editor = "Chunfeng Lian and Xiaohuan Cao and Islem Rekik and Xuanang Xu and Pingkun Yan",
booktitle = "Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings",
address = "Germany",
}