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The development of surgical navigation systems is critical in computer-aided surgery for achieving precision surgery. This study introduces an innovative framework for reconstructing patient-specific pneumoperitoneum models in Minimally Invasive Gastrointestinal Surgery (MIGS) navigation systems. Leveraging preoperative CT images and intraoperative 3D scan landmark data from 210 gastric cancer patients, we propose a data-driven approach to pneumoperitoneum reconstruction. Unlike conventional physics-based methods, our framework utilizes landmark displacement regression models to capture patient-specific deformation information. Furthermore, we utilize a CNN-based model to extract fat and muscle area from CT images and train the displacement regression model to reflect patient-specific characteristics. The efficacy of our approach is evaluated through comprehensive evaluation using traditional statistical and neural networks-based methods approaches. The code for reproducibility is available at github.com/PRIME-MICCAI24-APSP/APSP.

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Automated Patient-Specific Pneumoperitoneum Model Reconstruction for Surgical Navigation Systems in Distal Gastrectomy

  • 会议论文

  • 发表日期：

    2025年

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  • Saebom Shin,
  • Hye-su Jin,
  • Kyungyoon Jung,
  • Bokyung Park,
  • Jihun Yoon,
  • Sungjae Kim,
  • Jung-Eun Park,
  • Helen Hong,
  • Hansol Choi,
  • Seokrae Park,
  • Youngno Yoon,
  • Yoo Min Kim,
  • Min-Kook Choi,
  • Woo Jin Hyung

摘要

The development of surgical navigation systems is critical in computer-aided surgery for achieving precision surgery. This study introduces an innovative framework for reconstructing patient-specific pneumoperitoneum models in Minimally Invasive Gastrointestinal Surgery (MIGS) navigation systems. Leveraging preoperative CT images and intraoperative 3D scan landmark data from 210 gastric cancer patients, we propose a data-driven approach to pneumoperitoneum reconstruction. Unlike conventional physics-based methods, our framework utilizes landmark displacement regression models to capture patient-specific deformation information. Furthermore, we utilize a CNN-based model to extract fat and muscle area from CT images and train the displacement regression model to reflect patient-specific characteristics. The efficacy of our approach is evaluated through comprehensive evaluation using traditional statistical and neural networks-based methods approaches. The code for reproducibility is available at github.com/PRIME-MICCAI24-APSP/APSP.