Computerized segmentation of liver in hepatic CT and MRI by means of level-set geodesic active contouring

A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images

Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis. In this paper, we present a method for liver segmentation and a method for liver tumor segmentation. The two methods are grounded on a novel unified level set method (LSM), which incorporates both region information and edge information to evolve the contour. This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation. Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image. Then with manual setting of a few seed points on the obtained binary image, the following region-growing is performed to extract a rough liver region with no leakage. The unified LSM is proposed at last to refine the segmentation result. For liver tumor segmentation, a local intensity clustering based LSM coupled with hidden Markov random field and expectation-maximization (HMRF-EM) algorithm is applied to construct an enhanced edge indicator for the unified LSM. With this development, expected segmentation results can be obtained via the unified LSM, even for complex tumors. The two methods were evaluated with various datasets containing a local hospital dataset, the public datasets SLIVER07, 3Dircadb, and MIDAS via five measures. The proposed liver segmentation method outperformed other previous semiautomatic methods on the SLIVER07 dataset and required less interaction. The proposed liver tumor segmentation method was also competitive with other state-of-the-art methods in both accuracy and efficiency on the 3Dircadb database. Our methods are evaluated to be accurate and efficient, which allows their adoptions in clinical practice.

Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study

PURPOSE

To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume.

METHODS

This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and subsegmental liver volumes were segmented from contrast-enhanced 3D gradient-recalled echo MRI sequences and CT images. Semiautomated segmentation was based on variational interpolation and Laplacian mesh optimization. All segmentations were repeated after 2 weeks. Manual segmentation of CT images using an active contour tool was used as the reference standard. Repeatability and agreement of the methods were evaluated with intra-class correlation coefficients (ICC) and Bland-Altman analysis. Total interaction time was recorded.

RESULTS

Intra-reader ICC were ≥0.987 for MRI and ≥0.995 for CT. Intra-reader repeatability was 30 ± 217 ml (bias ± 1.96 SD) (95% limits of agreement: -187 to 247 ml) for MRI and -10 ± 143 ml (-153 to 133 ml) for CT. Inter-method ICC between semiautomated and manual volumetry were ≥0.995 for MRI and ≥0.986 for CT. Inter-method segmental ICC varied between 0.584 and 0.865 for MRI and between 0.596 and 0.890 for CT. Inter-method agreement was -14 ± 136 ml (-150 to 122 ml) for MRI and 50 ± 226 ml (-176 to 276 ml) for CT. Inter-method segmental agreement ranged from 10 ± 47 ml (-37 to 57 ml) to 2 ± 214 ml (-212 to 216 ml) for MRI and 9 ± 45 ml (-36 to 54 ml) to -46 ± 183 ml (-229 to 137 ml) for CT. Interaction time (mean ± SD) was significantly shorter for MRI-based semiautomated segmentation (7.2 ± 0.1 min, p < 0.001) and for CT-based semiautomated segmentation (6.5 ± 0.2 min, p < 0.001) than for CT-based manual segmentation (14.5 ± 0.4 min).

CONCLUSION

MRI-based semiautomated segmentation provides similar repeatability and agreement to CT-based segmentation for total liver volume.

Liver volumetry: Is imaging reliable? Personal experience and review of the literature

The amount of the future liver remnant volume is fundamental for hepato-biliary surgery, representing an important potential risk-factor for the development of post-hepatectomy liver failure. Despite this, there is no uniform consensus about the amount of hepatic parenchyma that can be safely resected, nor about the modality that should be chosen for this evaluation. The pre-operative evaluation of hepatic volume, along with a precise identification of vascular and biliar anatomy and variants, are therefore necessary to reduce surgical complications, especially for extensive resections. Some studies have tried to validate imaging methods [ultrasound, computed tomography (CT), magnetic resonance imaging] for the assessment of liver volume, but there is no clear evidence about the most accurate method for this evaluation. Furthermore, this volumetric evaluation seems to have a certain degree of error, tending to overestimate the actual hepatic volume, therefore some conversion factors, which should give a more reliable evaluation of liver volume, have been proposed. It is widespread among non-radiologists the use of independent software for an off-site volumetric analysis, performed on digital imaging and communications in medicine images with their own personal computer, but very few studies have provided a validation of these methods. Moreover, while the pre-transplantation volumetric assessment is fundamental, it remains unclear whether it should be routinely performed in all patients undergoing liver resection. In this editorial the role of imaging in the estimation of liver volume is discussed, providing a review of the most recent literature and a brief personal series of correlations between liver volumes and resection specimens’ weight, in order to assess the precision of the volumetric CT evaluation.