Segmentation of the Liver Using the Deformable Contour Method on CT Images

Lim, Seong-Jae; Jeong, Young-Ju; Ho, Yo‐Sung

doi:10.1007/11581772_50

Cited by 24 publications

(22 citation statements)

References 9 publications

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“…In most of the works based on gray-level statistics, a threshold is used to generate a binary volume that is later processed by morphological operators in order to separate desired organs. Recent gray-level methods have been presented by Soler et al (Soler et al, 2001), Fujimoto et al (Fujimoto et al, 2002), Liu et al (Liu et al, 2005) and Lim et al (Lim et al, 2004;Lim et al, 2005;Lim et al, 2006). However, the high variability among liver CT images due to the differences of intensity values in different kind of tumors and the different settings regarding contrast media, make difficult the optimal operation of the methods just based on gray-level statistics.…”

Section: State Of the Artmentioning

confidence: 99%

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Fernandez-de-Manuel¹,

Ledesma-Carbayo²,

Jiménez-Carretero³

et al. 2011

Theory and Applications of CT Imaging and Analysis

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Section: State Of the Artmentioning

confidence: 99%

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Fernandez-de-Manuel¹,

Ledesma-Carbayo²,

Jiménez-Carretero³

et al. 2011

Theory and Applications of CT Imaging and Analysis

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“…Region growing 4-6 finds a connected region by growing seed voxels on the condition that a homogeneity criterion (e.g., mean intensity or texture) remains unchanged when including a voxel to the region. Active contour and level set methods [7][8][9] evolve an initial contour iteratively with a speed function usually defined from gradient-based edge features which extract the contour toward the boundary. A set of parameters for controlling the shape of contour is also required in these contour evolution approaches.…”

Section: Introductionmentioning

confidence: 99%

Interactive semiautomatic contour delineation using statistical conditional random fields framework

Grossberg

et al. 2012

Med. Phys.

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Purpose: Contouring a normal anatomical structure during radiation treatment planning requires significant time and effort. The authors present a fast and accurate semiautomatic contour delineation method to reduce the time and effort required of expert users. Methods: Following an initial segmentation on one CT slice, the user marks the target organ and nontarget pixels with a few simple brush strokes. The algorithm calculates statistics from this information that, in turn, determines the parameters of an energy function containing both boundary and regional components. The method uses a conditional random field graphical model to define the energy function to be minimized for obtaining an estimated optimal segmentation, and a graph partition algorithm to efficiently solve the energy function minimization. Organ boundary statistics are estimated from the segmentation and propagated to subsequent images; regional statistics are estimated from the simple brush strokes that are either propagated or redrawn as needed on subsequent images. This greatly reduces the user input needed and speeds up segmentations. The proposed method can be further accelerated with graph-based interpolation of alternating slices in place of user-guided segmentation. CT images from phantom and patients were used to evaluate this method. The authors determined the sensitivity and specificity of organ segmentations using physician-drawn contours as ground truth, as well as the predicted-to-ground truth surface distances. Finally, three physicians evaluated the contours for subjective acceptability. Interobserver and intraobserver analysis was also performed and Bland-Altman plots were used to evaluate agreement. Results: Liver and kidney segmentations in patient volumetric CT images show that boundary samples provided on a single CT slice can be reused through the entire 3D stack of images to obtain accurate segmentation. In liver, our method has better sensitivity and specificity (0.925 and 0.995) than region growing (0.897 and 0.995) and level set methods (0.912 and 0.985) as well as shorter mean predicted-to-ground truth distance (2.13 mm) compared to regional growing (4.58 mm) and level set methods (8.55 mm and 4.74 mm). Similar results are observed in kidney segmentation. Physician evaluation of ten liver cases showed that 83% of contours did not need any modification, while 6% of contours needed modifications as assessed by two or more evaluators. In interobserver and intraobserver analysis, Bland-Altman plots showed our method to have better repeatability than the manual method while the delineation time was 15% faster on average. Conclusions: Our method achieves high accuracy in liver and kidney segmentation and considerably reduces the time and labor required for contour delineation. Since it extracts purely statistical information from the samples interactively specified by expert users, the method avoids heuristic assumptions commonly used by other methods. In addition, the method can be expanded to 3D directly without modificat...

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“…gray level based techniques, learning techniques, model fitting techniques, probabilistic atlases, and level set) this problem is still open. Indeed, although the gray level based techniques proposed so far [11,12,5,13,14,15,16,17] obtain the most promising results, they are not robust to database variations; this is because their basic step of organ gray level estimation does not take into account the high variability observed both in the same and in different CT volumes. For this reason, when tested on larger and complex data sets, these methods' performance could decrease significantly.…”

Section: Introductionmentioning

confidence: 99%

Automatic Abdominal Organ Segmentation from CT images

Casiraghi

Campadelli

Pratissoli

et al. 2009

ELCVIA

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In the recent years a great deal of research work has been devoted to the development of semi-automatic and automatic techniques for the analysis of abdominal CT images. Some of the current interests are the automatic diagnosis of liver, spleen, and kidney pathologies and the 3D volume rendering of the abdominal organs. The first and fundamental step in all these studies is the automatic organs segmentation, that is still an open problem. In this paper we propose our fully automatic system that employs a hierarchical gray level based framework to segment heart, bones (i.e. ribs and spine), liver and its blood vessels, kidneys, and spleen. The overall system has been evaluated on the data of 100 patients, obtaining a good assessment both by visual inspection by three experts, and by comparing the computed results to the boundaries manually traced by experts.

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Segmentation of the Liver Using the Deformable Contour Method on CT Images

Cited by 24 publications

References 9 publications

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Interactive semiautomatic contour delineation using statistical conditional random fields framework

Automatic Abdominal Organ Segmentation from CT images

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