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

Qin Liu1, Han Deng2, Chunfeng Lian1

  • 1Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, USA.

Machine Learning in Medical Imaging. MLMI (Workshop)
|December 29, 2021
PubMed
Summary
This summary is machine-generated.

SkullEngine automates bone segmentation and landmark detection for craniomaxillofacial surgery planning. This AI framework significantly reduces preparation time from hours to minutes, improving accuracy for complex cases.

Keywords:
Cone-Beam Computed Tomography (CBCT) ImageLandmark DetectionSegmentation

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in surgery
  • Craniomaxillofacial (CMF) reconstruction

Background:

  • Manual segmentation and landmark detection for CMF surgery planning are time-consuming, taking up to 12 hours per patient.
  • Current methods require significant surgeon expertise and are prone to variability.
  • Computer-aided surgical planning relies heavily on accurate anatomical data.

Purpose of the Study:

  • To develop an automated, efficient, and accurate framework for bone segmentation and landmark detection in CMF deformities.
  • To reduce the manual labor and time required for pre-surgical planning.
  • To improve the precision of segmentation and landmark identification for better surgical outcomes.

Main Methods:

  • A multi-stage, coarse-to-fine Convolutional Neural Network (CNN) based framework named SkullEngine was developed.
  • The framework utilizes a collaborative, integrated, and scalable Joint Segmentation and Detection (JSD) model.
  • Three refinement models were employed to enhance segmentation and landmark detection accuracy.

Main Results:

  • SkullEngine achieved high-resolution segmentation of the midface and mandible, and accurate detection of 175 landmarks.
  • Segmentation quality was significantly improved, particularly in thin bone regions.
  • Both segmentation and landmark detection tasks were completed simultaneously within 3 minutes for CBCT/CT images.

Conclusions:

  • SkullEngine offers a substantial improvement in efficiency and accuracy for CMF surgical planning.
  • The automated framework drastically reduces preparation time, enabling faster and potentially more precise surgical interventions.
  • Integration into clinical workflows is underway to further assess its real-world impact and efficiency.