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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한의용생체공학회 Biomedical Engineering Letters (BMEL) Biomedical Engineering Letters (BMEL) Vol.13 No.2
- 발행연도
- 2023.5
- 수록면
- 165 - 174 (10page)
- DOI
- https://doi.org/10.1007/s13534-023-00263-1
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초록· 키워드
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real-time for laparoscopic image-guided navigation. The stereo vision method for intraoperative tissue 3D reconstructionhas the most potential for clinical development benefiting from its high reconstruction accuracy and laparoscopy compatibility.
However, existing stereo vision methods have difficulty in achieving high reconstruction accuracy in real time. Also,intraoperative tissue reconstruction results often contain complex background and instrument information that preventsclinical development for image-guided systems. Taking laparoscopic partial nephrectomy (LPN) as the research object, thispaper realizes a real-time dense reconstruction and extraction of the kidney tissue surface. The central symmetrical Censusbased semi-global block stereo matching algorithm is proposed to generate a dense disparity map. A GPU-based pixel-bypixelconnectivity segmentation mechanism is designed to segment the renal tissue area. An in-vitro porcine heart, in-vivoporcine kidney and offline clinical LPN data were performed to evaluate the accuracy and effectiveness of our approach.
The algorithm achieved a reconstruction accuracy of ± 2 mm with a real-time update rate of 21 fps for an HD image size of960 × 540, and 91.0% target tissue segmentation accuracy even with surgical instrument occlusions. Experimental resultshave demonstrated that the proposed method could accurately reconstruct and extract renal surface in real-time in LPN. Themeasurement results can be used directly for image-guided systems. Our method provides a new way to measure geometricinformation of target tissue intraoperatively in laparoscopy surgery.
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