Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm
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We developed a medical image segmentation and preoperative planning application which implements a semiautomatic and a hybrid semiautomatic liver segmentation algorithm. The aim of this study was to evaluate the feasibility of computer-assisted liver tumor surgery using these algorithms which are based on thresholding by pixel intensity value from initial seed points. A random sample of 12 patients undergoing elective high-risk hepatectomies at our institution was prospectively selected to undergo computer-assisted surgery using our algorithms (June 2013–July 2014). Quantitative and qualitative evaluation was performed. The average computer analysis time (segmentation, resection planning, volumetry, visualization) was 45 min/dataset. The runtime for the semiautomatic algorithm was <0.2 s/slice. Liver volumetric segmentation using the hybrid method was achieved in 12.9 s/dataset (SD ± 6.14). Mean similarity index was 96.2 % (SD ± 1.6). The future liver remnant volume calculated by the application showed a correlation of 0.99 to that calculated using manual boundary tracing. The 3D liver models and the virtual liver resections had an acceptable coincidence with the real intraoperative findings. The patient-specific 3D models produced using our semiautomatic and hybrid semiautomatic segmentation algorithms proved to be accurate for the preoperative planning in liver tumor surgery and effectively enhanced the intraoperative medical image guidance.
KeywordsComputer-assisted surgery Liver segmentation Hepatectomy Liver neoplasms Liver tumor
This study was supported by a grant for Ph.D. studies from the Onassis Foundation. Thanks are due to Dr. G. Drosou (Biologist) for her indications on 3D visualization. Thanks are due to Mrs. A. Skoura (Computer Engineer) for her help in analysis of anatomical tree-shape structures.
This study was supported by a grant for Ph.D. studies from the Onassis Foundation.
Compliance with ethical standards
Conflict of interest
Author ZA has received research grants from the Onassis Foundation. All the other authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
- 3.Center for Integrative Biomedical Computing (CIBC) (2015) Seg3D: volumetric image segmentation and visualization. Scientific computing and imaging institute (SCI). http://www.seg3d.org. Accessed 5 Jan 2015
- 4.Dawant BM, Li R, Lennon B, Li S (2007) Semi-automatic segmentation of the liver and its evaluation on the MICCAI 2007 grand challenge data set. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 215–221Google Scholar
- 11.MATLAB—the language of technical computing—A. http://www.mathworks.com. Accessed 6 Nov 2014
- 14.SLIVER07: home. http://sliver07.org/. Accessed 27 Jan 2015