A region‐appearance‐based adaptive variational model for 3D liver segmentation

Peng, Jialin; Dong, Fangfang; Chen, Yunmei; Kong, Dexing

doi:10.1118/1.4866837

Cited by 36 publications

(38 citation statements)

References 25 publications

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“…A variational energy method (named RAP) which combined regional appearances, intensity and surface smoothness, was proposed in Ref. 5. This model showed sound results in dealing with fuzzy boundaries and heterogeneous backgrounds.…”

Section: Introductionmentioning

confidence: 99%

“…In line with segmentation procedures, liver segmentation methods can also be categorized into two classes: propagation approaches 7,8,11 based on 2D slices and direct 3D segmentation based methods. 2,5,6 For example, Lee et al 11 propagated the segmentation across adjacent 2D slices as the initialization and constraint for the active contour evolution. Afifi and Nakaguchi 7 used the segmentation from previous slice as constraints in the current slice.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, liver segmentation in 3D CT image can be grouped as prior-model-based approaches 3,4,[12][13][14][15] and image-data-driven approaches. [5][6][7][16][17][18][19] The first group, modelbased segmentation, is a global approach that matches a prior model into the target image. Popular methods for construction of prior models are statistical shape model (SSM) 3,14,20,21 and (probabilistic) atlas.…”

Section: Introductionmentioning

confidence: 99%

“…Commonly used methods include thresholding, 18 region growing, 19 deformable model-based approaches/variational energy minimization, 5,8 fast marching, 17 graph cuts, 7,9 and so on. Leakage on weak edges is one of the biggest challenges for methods in this class.…”

Section: Introductionmentioning

confidence: 99%

“…Current methods 2 commonly used for liver segmentation can be broadly grouped into three groups: automatic methods, 3,4 semiautomatic methods, [5][6][7][8] and interactive methods. 9,10 Automatic approaches are obviously more desirable than semiautomatic and interactive methods in that they call for no user interactions; however, semiautomatic and interactive approaches remain essential for the highly ambiguous liver segmentation in clinical settings.…”

Section: Introductionmentioning

confidence: 99%

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3D liver segmentation using multiple region appearances and graph cuts

Peng

et al. 2015

Medical Physics

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Purpose: Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty. Despite the significant intensity difference, liver tumors should be segmented as part of the liver. This study aims to address these challenges, especially when the target livers contain subregions with distinct appearances. Methods: The authors propose a novel multiregion-appearance based approach with graph cuts to delineate the liver surface. For livers with multiple subregions, a geodesic distance based appearance selection scheme is introduced to utilize proper appearance constraint for each subregion. A special case of the proposed method, which uses only one appearance constraint to segment the liver, is also presented. The segmentation process is modeled with energy functions incorporating both boundary and region information. Rather than a simple fixed combination, an adaptive balancing weight is introduced and learned from training sets. The proposed method only calls initialization inside the liver surface. No additional constraints from user interaction are utilized. Results: The proposed method was validated on 50 3D CT images from three datasets, i.e., Medical Image Computing and Computer Assisted Intervention (MICCAI) training and testing set, and local dataset. On MICCAI testing set, the proposed method achieved a total score of 83.4 ± 3.1, outperforming nonexpert manual segmentation (average score of 75.0). When applying their method to MICCAI training set and local dataset, it yielded a mean Dice similarity coefficient (DSC) of 97.7% ± 0.5% and 97.5% ± 0.4%, respectively. These results demonstrated the accuracy of the method when applied to different computed tomography (CT) datasets. In addition, user operator variability experiments showed its good reproducibility. Conclusions: A multiregion-appearance based method is proposed and evaluated to segment liver. This approach does not require prior model construction and so eliminates the burdens associated with model construction and matching. The proposed method provides comparable results with state-of-the-art methods. Validation results suggest that it may be suitable for the clinical use. C 2015 American Association of Physicists in Medicine. [http://dx

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D liver segmentation using multiple region appearances and graph cuts

Peng

et al. 2015

Medical Physics

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Fully automated liver segmentation using Sobolev gradient‐based level set evolution

Göçeri

2016

Numer Methods Biomed Eng

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Quantitative analysis and precise measurements on the liver have vital importance for pre-evaluation of surgical operations and require high accuracy in liver segmentation from all slices in a data set. However, automated liver segmentation from medical image data sets is more challenging than segmentation of any other organ due to various reasons such as vascular structures in the liver, high variability of liver shapes, similar intensity values, and unclear edges between liver and its adjacent organs. In this study, a variational level set-based segmentation approach is proposed to be efficient in terms of processing time and accuracy. The efficiency of this method is achieved by (1) automated initialization of a large initial contour, (2) using an adaptive signed pressure force function, and also (3) evolution of the level set with Sobolev gradient. Experimental results show that the proposed fully automated segmentation technique avoids local minima and stops evolution of the active contour at desired liver boundaries with high speed and accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

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Automatic liver segmentation by integrating fully convolutional networks into active contour models

2019

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Purpose Automatic and accurate three‐dimensional (3D) segmentation of liver with severe diseases from computed tomography (CT) images is a challenging task. Fully convolutional networks (FCNs) have emerged as powerful tools for automatic semantic segmentation, with multiple potential applications in medical imaging. However, the use of a large receptive field and multiple pooling layers in the network leads to poor localization around object boundaries. The network usually makes pixel‐wise prediction independently, making it difficult to respect local label consistency and enforce the smoothness of the object boundary. Methods We have developed an automatic liver segmentation method based on a novel framework that integrates fully convolutional network predictions into active contour models (ACM). We use only a single network architecture to generate a pixel label map containing spatial regional information (foreground and background) as well as layered boundary information. We exploit the structured network outcome to define an external constraint force of active contour models. A unique property of the designed force is that both its strength and direction are adaptive to its position and relative distance to the object boundary. The resulting integrated active contour models have the advantages of incorporating both high‐level and low‐level image information simultaneously, while enforcing the smoothness of the contour. Because the external constraint force can push the evolving contour to the liver boundary and exists everywhere in the image domain, it allows us to place the initial contour far away from the liver boundary. It potentially allows us to control the evolution of the contour in order to preserve the topology of the liver. Results We have trained and evaluated our model on 73 liver CT scans from a clinic study. The integrated ACM model yields mean dice coefficients (DICE) 95.8 ± 1.4 (%). Without further fine‐tuning the network weights for two independent datasets, it yields mean DICE 96.2 ± 0.9 (%) for the SLIVER07 training dataset, and mean DICE 94.3 ± 2.7 (%) for the LiTS training dataset. In comparison with FCN alone model, the integrated ACM model yields improvements in terms of surface distance and DICE values for almost all the cases. Furthermore, the initialization of the active contour can be very far away from the liver boundary. Conclusions Experimental results for segmenting livers (with severe diseases on CT images resulting in shape and density abnormalities) have revealed that our proposed model improves segmentation results in comparison with FCN alone. Without further fine‐tuning the network weights for two independent datasets, the model is capable of handling image variations from different datasets due to its inherent deformable nature. It is relatively easy to integrate more advanced (either existing or future) FCN architecture into our framework to further improve the segmentation performance.

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A region‐appearance‐based adaptive variational model for 3D liver segmentation

Abstract: The proposed model can efficiently delineate ambiguous liver edges from complex tissue backgrounds with reproducibility. Quantitative validations and comparative results demonstrate the accuracy and efficacy of the model.

Cited by 36 publications

References 25 publications

3D liver segmentation using multiple region appearances and graph cuts

3D liver segmentation using multiple region appearances and graph cuts

Fully automated liver segmentation using Sobolev gradient‐based level set evolution

Automatic liver segmentation by integrating fully convolutional networks into active contour models

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