Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images

Ruskó, László; Bekes, György; Fidrich, Márta

doi:10.1016/j.media.2009.07.009

Cited by 98 publications

(84 citation statements)

References 16 publications

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“…Another review of the methods for liver identification in CT images was published by Heimann et al (2009). Recent works in this area have also been described by Ruskó et al (2009), Masuda et al (2010 and Pu et al (2009). Linguraru et al (2009) proposed a method to isolate both the spleen and liver in contrast-enhanced CT images by first aligning the images with models from an atlas and then improving the results by an active contour technique.…”

Section: Liver Applicationsmentioning

confidence: 98%

Identification of Structures in Medical Images

Rebelo¹,

Furuie²,

Moura³

et al. 2011

Medical Imaging

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Section: Liver Applicationsmentioning

confidence: 98%

Identification of Structures in Medical Images

Rebelo¹,

Furuie²,

Moura³

et al. 2011

Medical Imaging

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“…To achieve the automatic segmentation, Ruskó et al 8 developed a region growing method that involves several pre-and postprocessing steps to eliminate oversegmentation. Rikxoort et al 9 and Klein et al 10 employed methods based on atlas matching.…”

Section: Related Workmentioning

confidence: 99%

A new segmentation framework based on sparse shape composition in liver surgery planning system

Wang

Zhang

et al. 2013

Medical Physics

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Purpose: To improve the accuracy and the robustness of the segmentation in living donor liver transplantation (LDLT) surgery planning system, the authors present a new segmentation framework that addresses challenges induced by the complex shape variations of patients' livers with cancer. It is designed to achieve the accurate and robust segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors in the LDLT surgery planning system. Methods: The segmentation framework proposed in this paper includes two important modules:(1) The robust shape prior modeling for liver, in which the sparse shape composition (SSC) model is employed to deal with the complex variations of liver shapes and obtain patient-specific liver shape priors. (2) The integration of the liver shape prior with a minimally supervised segmentation algorithm to achieve the accurate segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors simultaneously. The authors apply this segmentation framework to our previously developed LDLT surgery planning system to enhance its accuracy and robustness when dealing with complex cases of patients with liver cancer. Results: Compared with the principal component analysis, the SSC model shows a great advantage in handling the complex variations of liver shapes. It also effectively excludes gross errors and outliers that appear in the input shape and preserves local details for specific patients. The proposed segmentation framework was evaluated on the clinical image data of liver cancer patients, and the average symmetric surface distance for hepatic parenchyma, portal veins, hepatic veins, and tumors was 1.07 ± 0.76, 1.09 ± 0.28, 0.92 ± 0.35 and 1.13 ± 0.37 mm, respectively. The Hausdorff distance for these four tissues was 7.68, 4.67, 4.09, and 5.36 mm, respectively. Conclusions: The proposed segmentation framework improves the robustness of the LDLT surgery planning system remarkably when dealing with complex clinical liver shapes. The SSC model is able to handle non-Gaussian errors and preserve local detail information of the input liver shape. As a result, the proposed framework effectively addresses the problems caused by the complex shape variations of livers with cancer. Our framework not only obtains accurate segmentation results for healthy persons and common patients, but also shows high robustness when dealing with specific patients with large variations of liver shapes in complex clinical environments.

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“…Whilst methods exist to overcome this problem (Chen et al, 2009;Foruzana et al, 2009;Rusko et al, 2009), they are seldom satisfactory. Consequently, manual segmentation of organs such as the liver, whilst time consuming, is preferred.…”

Section: Liver Segmentationmentioning

confidence: 99%