Abstract
Purpose
Segmentation of liver tumours is an important part of the 3D visualisation of the liver anatomy for surgical planning. The spatial relationship between tumours and other structures inside the liver forms the basis of preoperative surgical risk assessment. However, the automatic segmentation of liver tumours from abdominal CT scans is riddled with challenges. Tumours located at the border of the liver impose a big challenge as the surrounding tissues could have similar intensities.
Methods
In this work, we introduce a fully automated liver tumour segmentation approach in contrast-enhanced CT datasets. The method is a multi-stage technique which starts with contrast enhancement of the tumours using anisotropic filtering, followed by adaptive thresholding to extract the initial mask of the tumours from an identified liver region of interest. Localised level set-based active contours are used to extend the mask to the tumour boundaries.
Results
The proposed method is validated on the IRCAD database with pathologies that offer highly variable and complex liver tumours. The results are compared quantitatively to the ground truth, which is delineated by experts. We achieved an average dice similarity coefficient of 75% over all patients with liver tumours in the database with overall absolute relative volume difference of 11%. This is comparable to other recent works, which include semiautomated methods, although they were validated on different datasets.
Conclusions
The proposed approach aims to segment tumours inside the liver envelope automatically with a level of accuracy adequate for its use as a tool for surgical planning using abdominal CT images. The approach will be validated on larger datasets in the future.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Blum HE (2005) Hepatocellular carcinoma: therapy and prevention. World J Gastroenterol 11:7391–400
Göçeri EA (2013) Comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function. Dissertation, İzmir Institute of Technology
Anter AM, Azar AT, Hassanien AE, El-Bendary N, Elsoud MA (2013) Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques. In: Federated conference on computer science and information systems (FedCSIS), 2013, pp 193–198
Göçeri E, Gürcan MN, Dicle O (2014) Fully automated liver segmentation from SPIR image series. Comput Biol Med 53:265–278
Priyadarsini S, Selvathi D (2012) Survey on segmentation of liver from CT images. In: Proceedings of 2012 IEEE international conference on advanced communication, control and computing technologies (ICACCCT), 2012, pp 234–238
Pamulapati V, Venkatesan A, Wood BJ, Linguraru MG (2012) Liver segmental anatomy and analysis from vessel and tumor segmentation via optimized graph cuts. In: Yoshida H, Sakas G, Linguraru MG (eds) Abdominal imaging. Computational and Clinical Applications. ABD-MICCAI 2011. Lecture Notes in Computer Science, vol 7029. Springer, Berlin, Heidelberg, pp 189–197
Göçeri E (2016) Fully automated liver segmentation using Sobolev gradient-based level set evolution. Int J Numer Methods Biomed Eng 32:e02765
Kumar SS, Moni RS, Rajeesh J (2013) Automatic liver and lesion segmentation: a primary step in diagnosis of liver diseases. Signal Image Video Process 7:163–172
Yingyi Q, Wei X, Wee KL, Qi T, Jiayin Z, Jiang L, Thazin H, Sudhakar KV, Shih-chang W (2008) Semi-automatic segmentation of liver tumors from CT scans using Bayesian rule-based 3D region growing. In: MICCAI workshop, vol 41, pp 1–10
Oliveira DA, Feitosa RQ, Correia MM (2011) Segmentation of liver, its vessels and lesions from CT images for surgical planning. Biomed Eng Online 10:30
Zhou JY, Wong DW, Ding F, Venkatesh SK, Tian Q, Qi YY, Xiong W, Liu JJ, Leow WK (2010) Liver tumour segmentation using contrast-enhanced multi-detector CT data: performance benchmarking of three semiautomated methods. Eur Radiol 20:1738–1748
Freiman M, Cooper O, Lischinski D, Joskowicz L (2011) Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation. Int J Comput Assist Radiol Surg 6:247–255
Moltz JH, Bornemann L, Dicken V, Peitgen HO (2008) Segmentation of liver metastases in CT scans by adaptive thresholding and morphological processing. In: MICCAI workshop, vol 41, p 195
Li BN, Chui CK, Chang S, Ong SH (2012) A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Syst Appl 39:9661–9668
Häme Y, Pollari M (2012) Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation. Med Image Anal 16:140–149
Huang W, Li N, Lin Z, Huang GB, Zong W, Zhou J, Duan Y (2013) Liver tumor detection and segmentation using Kernel-based extreme learning machine. In: 35th annual international conference of the IEEE EMBS Osaka, Japan, pp 3662–3665
Wu W, Wu S, Zhou Z, Zhang R, Zhang Y (2017) 3D liver tumor segmentation in CT images using improved fuzzy C-means and graph cuts. Biomed Res Int 2017:1–11
Linguraru MG, Richbourg WJ, Liu J, Watt JM, Pamulapati V, Wang S, Summers RM (2012) Tumor burden analysis on computed tomography by automated liver and tumor segmentation. IEEE Trans Med Imaging 31:1965–1976
Wu W, Wu S, Zhang R, Zhou Z (2016) Fast graph cuts based liver and tumor segmentation on olumetric CT images. In: Joint international conference on service science, management and engineering and international conference on information science and technology, pp 3–7
Brancatelli G, Baron RL, Peterson MS, Marsh W (2003) Helical CT screening for hepatocellular carcinoma in patients with cirrhosis: frequency and causes of false-positive interpretation. Am J Roentgenol 180:1007–1014
Mendrik AM, Vonken EJ, Rutten A, Viergever MA, Van Ginneken B (2009) Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch. IEEE Trans Med Imaging 28:1585–1594
Weickert J (1998) Anisotropic diffusion in image processing. Image Rochester NY 256:170
Irr OIA, Rahni AAA (2015) Automatic volumetric localization of the liver in abdominal CT scans using low level processing and shape priors. In: IEEE international conference on signal and image processing applications (ICSIPA), 2015, pp 434–438
Lankton S, Member S, Tannenbaum A (2008) Localizing region-based active contours. IEEE Trans Image Process 17(11):2029–2039
Kim KB, Kim CW, Kim GH (2008) Area extraction of the liver and hepatocellular carcinoma in CT scans. J Digit Imaging 21(Suppl 1):S89–103
Vorontsov E, Abi-Jaoudeh N, Kadoury S (2014) Metastatic liver tumor segmentation using texture-based omni-directional deformable surface models. Springer, Cham, pp 74–83
Funding
This research is supported by the Malaysian Ministry of Higher Education and Universiti Kebangsaan Malaysia (Grant Number GUP-2014-066).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
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
Informed consent was obtained from all individual participants included in the study.
Rights and permissions
About this article
Cite this article
Alirr, O.I., Rahni, A.A.A. & Golkar, E. An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning. Int J CARS 13, 1169–1176 (2018). https://doi.org/10.1007/s11548-018-1801-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-018-1801-z