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Automated liver lesion detection in CT images based on multi-level geometric features

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

Abstract

Purpose

Due to the increasing number of liver cancer cases in clinical practice, there is a significant need for efficient tools for computer-assisted liver lesion analysis. A wide range of clinical applications, such as lesion characterization, quantification and follow-up, can be facilitated by automated liver lesion detection. Liver lesions vary significantly in size, shape, density and heterogeneity, which make them difficult to detect automatically. The goal of this work was to develop a method that can detect all types of liver lesions with high sensitivity and low false positive rate within a short run time.

Methods

The proposed method identifies abnormal regions in liver CT images based on their intensity using a multi-level segmentation approach. The abnormal regions are analyzed from the inside-out using basic geometric features (such as asymmetry, compactness or volume). Using this multi-level shape characterization, the abnormal regions are classified into lesions and other region types (including vessel, liver boundary). The proposed analysis also allows defining the contour of each finding. The method was trained on a set of 55 cases involving 120 lesions and evaluated on a set of 30 images involving 59 (various types of) lesions, which were manually contoured by a physician.

Results

The proposed algorithm demonstrated a high detection rate (92 %) at a low (1.7) false positive per case (precision 51 %), when the method was started from a manually contoured liver. The same level of false positive per case (1.6) and precision (51 %) was achieved at a somewhat lower detection rate (85 %), when the volume of interest was defined by a fully automated liver segmentation.

Conclusions

The proposed method can efficiently detect liver lesions irrespective of their size, shape, density and heterogeneity within half a minute. According to the evaluation, its accuracy is competitive with the actual state-of-the-art approaches, and the contour of the detected findings is acceptable in most of the cases. Future work shall focus on more precise lesion contouring so that the proposed method can be a solid basis for fully automated liver tumour burden estimation.

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Acknowledgments

This work was supported by the research and development fund GOP-1.1.1-08/1-2008-0037 of the National Development Agency of Hungary. Hereby, the authors would like to thank Dr. Zsolt Berényi MD and Prof. Dr. András Palkó MD from the Radiology Department, Faculty of Medicine, University of Szeged for their feedback about clinical liver lesion assessment.

Conflict of interest

Authors, László Ruskó and Ádám Perényi declare that they have no conflict of interest. Co-author, Dr. Ádám Perényi, as well as her institution, University of Szeged, Department of Radiology was financially supported by GE Hungary Healthcare division within the framework of the research and development tender GOP-1.1.1-08/1-2008-0037 of the National Development Agency of Hungary.

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Authors and Affiliations

  1. GE Hungary Healthcare Division, Petőfi Sándor sgt. 10, 6722 , Szeged, Hungary

    László Ruskó

  2. Department of Radiology, University of Szeged, Semmelweis utca 6., 6725 , Szeged, Hungary

    Ádám Perényi

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  1. László Ruskó
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  2. Ádám Perényi
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Correspondence to László Ruskó.

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Ruskó, L., Perényi, Á. Automated liver lesion detection in CT images based on multi-level geometric features. Int J CARS 9, 577–593 (2014). https://doi.org/10.1007/s11548-013-0949-9

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  • DOI: https://doi.org/10.1007/s11548-013-0949-9

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