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Pelvic Fracture Reduction Planning Based on Morphable Models and Structural Constraints

  • Sutuke Yibulayimu
  • , Yanzhen Liu
  • , Yudi Sang
  • , Gang Zhu
  • , Yu Wang*
  • , Jixuan Liu
  • , Chao Shi
  • , Chunpeng Zhao
  • , Xinbao Wu
  • *Corresponding author for this work
  • Beihang University
  • Beijing Rossum Robot Technology Co., Ltd
  • Capital Medical University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As one of the most challenging orthopedic injuries, pelvic fractures typically involve iliac and sacral fractures as well as joint dislocations. Structural repair is the most crucial phase in pelvic fracture surgery. Due to the absence of data for the intact pelvis before fracture, reduction planning heavily relies on surgeon’s experience. We present a two-stage method for automatic reduction planning to restore the healthy morphology for complex pelvic trauma. First, multiple bone fragments are registered to morphable templates using a novel SSM-based symmetrical complementary (SSC) registration. Then the optimal target reduction pose of dislocated bone is computed using a novel articular surface (AS) detection and matching method. A leave-one-out experiment was conducted on 240 simulated samples with six types of pelvic fractures on a pelvic atlas with 40 members. In addition, our method was tested in four typical clinical cases corresponding to different categories. The proposed method outperformed traditional SSM, mean shape reference, and contralateral mirroring methods in the simulation experiment, achieving a root-mean-square error of 3.4 ± 1.6 mm, with statistically significant improvement. In the clinical feasibility experiment, the results on various fracture types satisfied clinical requirements on distance measurements and were considered acceptable by senior experts. We have demonstrated the benefit of combining morphable models and structural constraints, which simultaneously utilizes cohort statistics and patient-specific features.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
EditorsHayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
PublisherSpringer Science and Business Media Deutschland GmbH
Pages322-332
Number of pages11
ISBN (Print)9783031439957
DOIs
StatePublished - 2023
Event26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada
Duration: 8 Oct 202312 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14228 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Country/TerritoryCanada
CityVancouver
Period8/10/2312/10/23

Keywords

  • Morphable models
  • Pelvic fracture
  • Surgery planning

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