Abstract
Objective
Automatic tumour segmentation and volumetry is useful in cancer staging and treatment outcome assessment. This paper presents a performance benchmarking study on liver tumour segmentation for three semiautomatic algorithms: 2D region growing with knowledge-based constraints (A1), 2D voxel classification with propagational learning (A2) and Bayesian rule-based 3D region growing (A3).
Methods
CT data from 30 patients were studied, and 47 liver tumours were isolated and manually segmented by experts to obtain the reference standard. Four datasets with ten tumours were used for algorithm training and the remaining 37 tumours for testing. Three evaluation metrics, relative absolute volume difference (RAVD), volumetric overlap error (VOE) and average symmetric surface distance (ASSD), were computed based on computerised and reference segmentations.
Results
A1, A2 and A3 obtained mean/median RAVD scores of 17.93/10.53%, 17.92/9.61% and 34.74/28.75%, mean/median VOEs of 30.47/26.79%, 25.70/22.64% and 39.95/38.54%, and mean/median ASSDs of 2.05/1.41 mm, 1.57/1.15 mm and 4.12/3.41 mm, respectively. For each metric, we obtained significantly lower values of A1 and A2 than A3 (P < 0.01), suggesting that A1 and A2 outperformed A3.
Conclusions
Compared with the reference standard, the overall performance of A1 and A2 is promising. Further development and validation is necessary before reliable tumour segmentation and volumetry can be widely used clinically.
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Acknowledgements
This work is supported by a research grant (SBIC RP C-008/2006) from the Singapore Bio-Imaging Consortium, Agency for Science, Technology and Research, Singapore. The authors would like to thank Prof Shih-chang Wang (Sydney Medical School, University of Sydney, Australia) and Dr Thazin Han (Department of Diagnostic Radiology, National University of Singapore, Singapore) for their support and help. We also would like to thank Dr Xiang Deng and Dr Guangwei Du (Centre for Medical Imaging Validation, Siemens Corporate Technology, China) for their great effort in organising the LTSC and maintaining the competition website.
Part of the paper was presented at the European Congress of Radiology, 6–10 March 2009, Vienna, Austria.
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Zhou, JY., Wong, D.W.K., Ding, F. et al. Liver tumour segmentation using contrast-enhanced multi-detector CT data: performance benchmarking of three semiautomated methods. Eur Radiol 20, 1738–1748 (2010). https://doi.org/10.1007/s00330-010-1712-z
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DOI: https://doi.org/10.1007/s00330-010-1712-z