This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

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An automatic method that can segment the liver in contrast-enhanced MR LAVA images was developed and tested. The results demonstrate that the method is feasible, efficient and robust to artifacts and pathology.

Both manual and automated multiphase MDCT-based volume measurements were strongly correlated to liver volume (Pearson correlation coefficient, r = 0.87 [p < 0.0001] and 0.90 [p < 0.0001], respectively). Automated multiphase segmentation was significantly more rapid than manual segmentation (mean time, 16 ± 5 [SD] and 86 ± 3 seconds, respectively; p = 0.01). Overall, automated liver volumetry based on multiphase CT acquisitions is feasible and more rapid than manual segmentation.

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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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