SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection

Chunfeng Lian; Deqiang Xiao; Han Deng; Jaime Gateno; James J. Xia; Lei Ma; Pew-Thian Yap; Qin Liu; Tianshu Kuang; Xiaoyang Chen

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SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection

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arxiv 2110.03828 v2 pith:KVHXQRGS submitted 2021-10-07 eess.IV cs.CV

SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection

Qin Liu , Han Deng , Chunfeng Lian , Xiaoyang Chen , Deqiang Xiao , Lei Ma , Xu Chen , Tianshu Kuang

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Jaime Gateno Pew-Thian Yap James J. Xia

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classification eess.IV cs.CV

keywords detectionframeworklandmarksegmentationbonescbctcollaborativemulti-stage

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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.

SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection

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