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Articular surface segmentation using active shape models for intraoperative implant assessment.

Joseph Görres1, Michael Brehler2, Jochen Franke3

  • 1Division of Medical and Biological Informatics (E130), German Cancer Research Center, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany. joseph.goerres@jhu.edu.

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|April 21, 2016
PubMed
Summary

This study introduces an automatic method using cone-beam computed tomography (CBCT) to detect intra-articular implants in orthopedic surgery, improving accuracy and reducing revision rates.

Keywords:
Articular surfaceCBCTCalcaneusCuboidInternal fixationIntraoperative imagingOpen reductionSegmentationShape modelTalus

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Area of Science:

  • Orthopedic Surgery
  • Medical Imaging
  • Computational Anatomy

Background:

  • Intra-articular implant placement in orthopedic surgery is linked to higher revision rates and arthritis risk.
  • Accurate intraoperative assessment is crucial for preventing such complications.

Purpose of the Study:

  • To develop and validate an automatic detection approach for intra-articular implants using cone-beam computed tomography (CBCT).
  • To support intraoperative assessment and correction of surgical implant placement.

Main Methods:

  • Utilized multiple active shape models (ASM) to segment joint spaces and articular surfaces.
  • Integrated fast and robust methods within ASM for intraoperative accuracy.
  • Employed cylinder detection to identify metal implants and their intersections with articular surfaces to detect intra-articular collisions.

Main Results:

  • Achieved average surface distance errors of 0.59 mm and 0.46 mm for calcaneal articular surface segmentations on 50 patient images.
  • Demonstrated a 22% and 25% average error reduction using model-independent segmentation.
  • Localized articular perforations in cadaver tests with an average accuracy of 0.80 mm, compensating for significant translation and rotation errors.

Conclusions:

  • Presented a novel concept for automatic intraoperative detection of intra-articular implants in CBCT images.
  • Validated reliable segmentation of articular surfaces in patient data.
  • Confirmed accurate localization of misplaced implants in cadaver tests.