Automatic atlas-based liver segmental anatomy identification for hepatic surgical planning
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For the liver to remain viable, the resection during hepatectomy procedure should proceed along the major vessels; hence, the resection planes of the anatomic segments are defined, which mark the peripheries of the self-contained segments inside the liver. Liver anatomic segments identification represents an essential step in the preoperative planning for liver surgical resection treatment.
The method based on constructing atlases for the portal and the hepatic veins bifurcations, the atlas is used to localize the corresponding vein in each segmented vasculature using atlas matching. Point-based registration is used to deform the mesh of atlas to the vein branch. Three-dimensional distance map of the hepatic veins is constructed; the fast marching scheme is applied to extract the centerlines. The centerlines of the labeled major veins are extracted by defining the starting and the ending points of each labeled vein. Centerline is extracted by finding the shortest path between the two points. The extracted centerline is used to define the trajectories to plot the required planes between the anatomical segments.
The proposed approach is validated on the IRCAD database. Using visual inspection, the method succeeded to extract the major veins centerlines. Based on that, the anatomic segments are defined according to Couinaud segmental anatomy.
Automatic liver segmental anatomy identification assists the surgeons for liver analysis in a robust and reproducible way. The anatomic segments with other liver structures construct a 3D visualization tool that is used by the surgeons to study clearly the liver anatomy and the extension of the cancer inside the liver.
KeywordsLiver segmental anatomy Surgical planning Atlas-based identification Abdominal CT
This research is supported by the Malaysian Ministry of Higher Education and Universiti Kebangsaan Malaysia (Grant No. GUP-2014-066).
Compliance with ethical standards
Conflict of interest
The 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. For this type of study, formal consent is not required.
Informed consent was obtained from all individual participants included in the study.
- 1.Yoon JH, Lee JM, Jun JH, Suh KS, Coulon P, Han JK, Choi BI (2014) Feasibility of three-dimensional virtual surgical planning in living liver donors. Abdom Imaging 40:1–11Google Scholar
- 4.Pamulapati V, Venkatesan A, Wood BJ, Linguraru MG (2012) Liver segmental anatomy and analysis from vessel and tumor segmentation via optimized graph cuts. In: International MICCAI workshop on computational and clinical challenges in abdominal imaging. Springer, Berlin, pp 189–197Google Scholar
- 5.Alirr OI, Rahni AAA (2019) Survey on liver tumour resection planning system: steps, techniques, and parameters. J Digit Imaging 1–20Google Scholar
- 11.Rodrigues FM, Silva JS, Rodrigues TM (2012) An algorithm for the surgical planning of hepatic resections. In: 2012 IEEE 2nd Portuguese meeting in bioengineering ENBENG 2012Google Scholar
- 16.Debarba HG, Zanchet DJ, Fracaro D, MacIel A, Kalil AN (2010) Efficient liver surgery planning in 3D based on functional segment classification and volumetric information. In: 2010 Annual international conference of the IEEE engineering in medical biology EMBC’10, pp 4797–4800Google Scholar
- 21.Mokry T, Bellemann N, Müller D, Bermejo JL, Klauß M, Stampfl U, Radeleff B, Schemmer P, Kauczor HU, Sommer CM (2014) Accuracy of estimation of graft size for living-related liver transplantation: first results of a semi-automated interactive software for CT-volumetry. PLoS ONE 9:e110201CrossRefGoogle Scholar
- 26.Ibrahim OI, Irr A, Aizzuddin A (2015) Automatic volumetric localization of the liver in abdominal CT scans using low level processing and shape priors. In: 2015 IEEE international conference signal and image processing applications, pp 434–438Google Scholar
- 27.Alirr OI, Rahni AA (2018) Automatic liver segmentation from ct scans using intensity analysis and level-set active contours. J Eng Sci Technol 13(11):3821–3839Google Scholar
- 28.Irr OIA, Rahni AAA (2015) Automatic volumetric localization of the liver in abdominal CT scans using low level processing and shape priors. In: 2015 IEEE international conference signal and image processing application, pp 434–438Google Scholar
- 31.Low K (2004) Linear least-squares optimization for point-to-plane ICP surface registration. University of North Carolina, Chapel Hill, pp 2–4Google Scholar
- 33.Lillah M, Boisvert J (2012) Centerline extraction with fast marching methods. CCG annual report 14Google Scholar
- 34.Alirr OI, Rahni AAA (2016) Development of automatic segmentation of the inferior vena cava in abdominal CT scans. In: 2016 IEEE EMBS conference on biomedical engineering and sciences (IECBES). IEEE, pp 235–239Google Scholar