Semi-automatic Liver Segmentation in CT Images Through Intensity Separation and Region Growing

Zheng, Zhou; Zhang, Xuechang; Zheng, Shi; Xu, Hongwei; Shi, Yong

doi:10.1016/j.procs.2018.04.206

Cited by 14 publications

(9 citation statements)

References 4 publications

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“…In recent years, to overcome the segmentation difficulty of tumor and liver due to low contrast, irregular shape and the fuzzy boundary between liver tissue and touching organs from CT images many highly sophisticated methods have been developed. These sophisticated frameworks can be classified into one of the three main categories on the basis of their characteristics, including semi-automatic strategies (Yang et al 2014;Bakas et al 2017;Kavur et al 2020), interactive approaches (Baâzaoui et al 2017;Chartrand et al 2017;Zhou et al 2018), and automatic frameworks (Li et al 2013;Ranjbarzadeh & Baseri Saadi 2020). The problems of the over-segmentation and leakage were overcomed using a semi-automatic approach consisting of three steps in (Xu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…In the field of the tumour and liver analysis, current algorithms mainly can be split into two broad groups, including anti-learning and learning methods (Lu et al 2017). The anti-learning methods regularly include (Luo, Li & Li 2014) the active contour (Guo, Schwartz & Zhao 2019;Xu et al 2020), clustering (Cai 2019;Ranjbarzadeh & Baseri Saadi 2020), region growing (Lu et al 2014;Zhou et al 2018;Zeng et al 2018;Liu et al 2019), graph cut (Liao et al 2016;Huang et al 2018;Liu et al 2019), and level set (Hoogi et al 2017; algorithms. Region growing approach selects the touching pixels with a high degree of similarity in intensity or variance value as the same object or area.…”

Section: Introductionmentioning

confidence: 99%

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TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Aghamohammadi

Ranjbarzadeh

Naiemi

et al. 2021

Expert Systems with Applications

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Automatic liver and tumour segmentation in CT images are crucial in numerous clinical applications, such as postoperative assessment, surgical planning, and pathological diagnosis of hepatic diseases. However, there are still a considerable number of difficulties to overcome due to the fuzzy boundary, irregular shapes, and complex tissues of the liver. In this paper, for liver and tumor segmentation and to overcome the mentioned challenges a simple but powerful strategy is presented based on a cascade convolutional neural network. At the first, the input image is normalized using the Z-Score algorithm. This normalized image provides more information about the boundary of tumor and liver. Also, the Local Direction of Gradient (LDOG) which is a novel encoding algorithm is proposed to demonstrate some key features inside the image. The proposed encoding image is highly effective in recognizing the border of liver, even in the regions close to the touching organs. Then, a cascade CNN structure for extracting both local and semi-global features is used which utilized the original image and two other obtained images as the input data.Rather than using a complex deep CNN model with a lot of hyperparameters, we employ a simple but effective model to decrease the train and testing time. Our technique outperforms the state-ofthe-art works in terms of segmentation accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Aghamohammadi

Ranjbarzadeh

Naiemi

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…Computed tomography (CT) images, magnetic resonance imaging (MRI) and ultrasound (US) images are the widely used modalities for segmentation. Semiautomatic and fully automatic segmentation methods performed on these modalities using different techniques has been an active area of research for a long time [4]. However, there are still certain challenges to be overcome while performing medical image segmentation, especially for those organs like the liver that have a remarkable intensity similarity with the adjacent organs like heart, stomach and spleen.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are still certain challenges to be overcome while performing medical image segmentation, especially for those organs like the liver that have a remarkable intensity similarity with the adjacent organs like heart, stomach and spleen. Also, intensity in-homogeneity often contributed by imaging artifacts and pathological conditions can make the process challenging [4].…”

Section: Introductionmentioning

confidence: 99%

Numerical Evaluation on Parametric Choices Influencing Segmentation Results in Radiology Images—A Multi-Dataset Study

et al. 2021

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Medical image segmentation has gained greater attention over the past decade, especially in the field of image-guided surgery. Here, robust, accurate and fast segmentation tools are important for planning and navigation. In this work, we explore the Convolutional Neural Network (CNN) based approaches for multi-dataset segmentation from CT examinations. We hypothesize that selection of certain parameters in the network architecture design critically influence the segmentation results. We have employed two different CNN architectures, 3D-UNet and VGG-16, given that both networks are well accepted in the medical domain for segmentation tasks. In order to understand the efficiency of different parameter choices, we have adopted two different approaches. The first one combines different weight initialization schemes with different activation functions, whereas the second approach combines different weight initialization methods with a set of loss functions and optimizers. For evaluation, the 3D-UNet was trained with the Medical Segmentation Decathlon dataset and VGG-16 using LiTS data. The quality assessment done using eight quantitative metrics enhances the probability of using our proposed strategies for enhancing the segmentation results. Following a systematic approach in the evaluation of the results, we propose a few strategies that can be adopted for obtaining good segmentation results. Both of the architectures used in this work were selected on the basis of general acceptance in segmentation tasks for medical images based on their promising results compared to other state-of-the art networks. The highest Dice score obtained in 3D-UNet for the liver, pancreas and cardiac data was 0.897, 0.691 and 0.892. In the case of VGG-16, it was solely developed to work with liver data and delivered a Dice score of 0.921. From all the experiments conducted, we observed that two of the combinations with Xavier weight initialization (also known as Glorot), Adam optimiser, Cross Entropy loss (GloCEAdam) and LeCun weight initialization, cross entropy loss and Adam optimiser LecCEAdam worked best for most of the metrics in a 3D-UNet setting, while Xavier together with cross entropy loss and Tanh activation function (GloCEtanh) worked best for the VGG-16 network. Here, the parameter combinations are proposed on the basis of their contributions in obtaining optimal outcomes in segmentation evaluations. Moreover, we discuss that the preliminary evaluation results show that these parameters could later on be used for gaining more insights into model convergence and optimal solutions.The results from the quality assessment metrics and the statistical analysis validate our conclusions and we propose that the presented work can be used as a guide in choosing parameters for the best possible segmentation results for future works.

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“…Many automatic segmentation methods of medical images have been proposed. The methods include thresholding [ 21 , 22 ], watershed [ 23 , 24 ], random walk [ 25 ], active contour models [ 25 – 27 ], statistical shape model [ 22 ], level-set [ 28 , 29 ], graph cuts [ 22 , 30 – 34 ], deformable models [ 35 ], region growing [ 29 , 36 , 37 ], and deep learning (DL) [ 33 , 34 , 38 – 41 ]. Regarding semi-automatic segmentation, human intervention is needed, such as manual arbitrary selection of the ROI, initialization of a seed point for region growing or level sets, contour for an active contour model or Laplacian mesh optimization, and seed nodes for random walk [ 28 , 30 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data

et al. 2021

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Background To estimate median liver iron concentration (LIC) calculated from magnetic resonance imaging, excluded vessels of the liver parenchyma region were defined manually. Previous works proposed the automated method for excluding vessels from the liver region. However, only user-defined liver region remained a manual process. Therefore, this work aimed to develop an automated liver region segmentation technique to automate the whole process of median LIC calculation. Methods 553 MR examinations from 471 thalassemia major patients were used in this study. LIC maps (in mg/g dry weight) were calculated and used as the input of segmentation procedures. Anatomical landmark data were detected and used to restrict ROI. After that, the liver region was segmented using fuzzy c-means clustering and reduced segmentation errors by morphological processes. According to the clinical application, erosion with a suitable size of the structuring element was applied to reduce the segmented liver region to avoid uncertainty around the edge of the liver. The segmentation results were evaluated by comparing with manual segmentation performed by a board-certified radiologist. Results The proposed method was able to produce a good grade output in approximately 81% of all data. Approximately 11% of all data required an easy modification step. The rest of the output, approximately 8%, was an unsuccessful grade and required manual intervention by a user. For the evaluation matrices, percent dice similarity coefficient (%DSC) was in the range 86–92, percent Jaccard index (%JC) was 78–86, and Hausdorff distance (H) was 14–28 mm, respectively. In this study, percent false positive (%FP) and percent false negative (%FN) were applied to evaluate under- and over-segmentation that other evaluation matrices could not handle. The average of operation times could be reduced from 10 s per case using traditional method, to 1.5 s per case using our proposed method. Conclusion The experimental results showed that the proposed method provided an effective automated liver segmentation technique, which can be applied clinically for automated median LIC calculation in thalassemia major patients.

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Semi-automatic Liver Segmentation in CT Images Through Intensity Separation and Region Growing

Cited by 14 publications

References 4 publications

TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Numerical Evaluation on Parametric Choices Influencing Segmentation Results in Radiology Images—A Multi-Dataset Study

An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data

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