Segmentation of liver, its vessels and lesions from CT images for surgical planning

Abstract

BackgroundCancer treatments are complex and involve different actions, which include many times a surgical procedure. Medical imaging provides important information for surgical planning, and it usually demands a proper segmentation, i.e., the identification of meaningful objects, such as organs and lesions. This study proposes a methodology to segment the liver, its vessels and nodules from computer tomography images for surgical planning.MethodsThe proposed methodology consists of four steps executed sequentially: segmentation of liver, segmentation of vessels and nodules, identification of hepatic and portal veins, and segmentation of Couinaud anatomical segments. Firstly, the liver is segmented by a method based on a deformable model implemented through level sets, of which parameters are adjusted by using a supervised optimization procedure. Secondly, a mixture model is used to segment nodules and vessels through a region growing process. Then, the identification of hepatic and portal veins is performed using liver anatomical knowledge and a vein tracking algorithm. Finally, the Couinaud anatomical segments are identified according to the anatomical liver model proposed by Couinaud.ResultsExperiments were conducted using data and metrics brought from the liver segmentation competition held in the Sliver07 conference. A subset of five exams was used for estimation of segmentation parameter values, while 15 exams were used for evaluation. The method attained a good performance in 17 of the 20 exams, being ranked as the 6th best semi-automatic method when comparing to the methods described on the Sliver07 website (2008). It attained visual consistent results for nodules and veins segmentation, and we compiled the results, showing the best, worst, and mean results for all dataset.ConclusionsThe method for liver segmentation performed well, according to the results of the numerical evaluation implemented, and the segmentation of liver internal structures were consistent with the anatomy of the liver, as confirmed by a specialist. The analysis provided evidences that the method to segment the liver may be applied to segment other organs, especially to those whose distribution of voxel intensities is nearly Gaussian shaped.

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

“…In [22], liver vessel segmentation is performed with regiongrowing in CT images. A pixel is incorporated in the growing region if its intensity falls in a predefined range.…”

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Review on the Methods of Automatic Liver Segmentation from Abdominal Images

“…It is efficient when the background is simple and the boundary between background and object is clear. In many cases, the segmentation result of one slice could be used as an initial segmentation of the adjacent slice [20,21]. To achieve better segmentation results, the original level set algorithm could be optimized.…”

A new segmentation framework based on sparse shape composition in liver surgery planning system

“…Zheng et al 17 also developed a graph cut-based segmentation algorithm to refine coarse manual segmentation results of liver tumors. Oliveira et al 18 proposed an effective deformable segmentation model based on level set for surgical planning. However, it is implemented at each slice sequentially.…”