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

Authors
 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 
Citation
 Lecture Notes in Computer Science, Vol.15155 LNCS : 74-85, 2024-10 
Journal Title
Lecture Notes in Computer Science
ISSN
 0302-9743 
Issue Date
2024-10
Abstract
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.
Full Text
https://link.springer.com/chapter/10.1007/978-3-031-74561-4_7
DOI
10.1007/978-3-031-74561-4_7
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
Yonsei Authors
Hyung, Woo Jin(형우진) ORCID logo https://orcid.org/0000-0002-8593-9214
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/202375
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