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Robust extraction for low-contrast liver tumors using modified adaptive likelihood estimation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Liver tumor extraction is essential for liver ablation surgery planning and treatment. For accurate and robust tumor segmentation, we propose a semiautomatic method using adaptive likelihood classification with modified likelihood model.

Methods

First, a minimal ellipse (or quasi-ellipsoid) that encloses a liver tumor is generated for initialization. Then, a hybrid intensity likelihood modification based on nonparametric density estimation is proposed to enhance local likelihood contrast and reduce its inhomogeneity. A prior elliptical (or quasi-ellipsoid) shape constraint is directly integrated into the likelihood to further prevent leakage of the algorithm into adjacent tissues with similar intensity. Finally, an adaptive likelihood classification is proposed for accurate segmentation of tumors with low contrast, high noise or heterogeneous densities.

Results

Experiments were performed on 3Dircadb and LiTS datasets. The average volumetric overlap errors of the 3Dircadb and LiTS datasets were 27.05 and 35.72%, respectively. The algorithm’s robustness was validated by comparing results of 5 operators with multiple selections on different tumors.

Conclusions

The proposed method achieved good results in different tumors, even in low-contrast tumors with blurred boundaries. Reliable results can still be achieved over different initializations by different operators using the proposed method.

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Notes

  1. http://www.ircad.fr/research/3d-ircadb-01.

  2. https://competitions.codalab.org/competitions/17094.

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Acknowledgements

The authors appreciate Julia Wu from MIT for English correction and acknowledge the support of the National Nature Science Foundation of China (Grant Number 81471759).

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Authors and Affiliations

  1. Room C249, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, People’s Republic of China

    Qing Huang, Hui Ding & Guangzhi Wang

  2. Department of Interventional Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, 100142, People’s Republic of China

    Xiaodong Wang

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  1. Qing Huang
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  2. Hui Ding
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  3. Xiaodong Wang
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  4. Guangzhi Wang
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Correspondence to Guangzhi Wang.

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Huang, Q., Ding, H., Wang, X. et al. Robust extraction for low-contrast liver tumors using modified adaptive likelihood estimation. Int J CARS 13, 1565–1578 (2018). https://doi.org/10.1007/s11548-018-1820-9

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  • DOI: https://doi.org/10.1007/s11548-018-1820-9

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