Liver CT sequence segmentation based with improved U-Net and graph cut

Liu, Zhe; Song, Yuqing; Sheng, Victor S.; Wang, Liangmin; Jiang, Rui; Zhang, Xiaolin; Yuan, Deqi

doi:10.1016/j.eswa.2019.01.055

Cited by 126 publications

(71 citation statements)

References 14 publications

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“…And those images were preprocessed using the same preprocessing technique that was implemented on the training data. The result of the network was evaluated using the respective ground truths of the images and the comparison result of this algorithm with works of Christ et al who had used a cascaded deep neural network with a 3D conditional random fields to segment liver and its lesions [12], Liu et al who came up with GIU-Net that combines the improved UNet with the graph cut algorithm for segmenting liver sequence images [10] and lastly with Budak et al who developed two cascaded encoder-decoder convolutional neural network for the segmentation of liver and its tumor [15], were also included.…”

Section: A Test Results For Liver Segmentationmentioning

confidence: 99%

“…They used different metrics for evaluating the performance of their algorithm and using the DSC they got 0.9505 value. In this paper, the authors only provide an algorithm for liver segmentation they didn't forward a solution for segmenting its pathologies [10].…”

Section: Related Work In the Area Of The Researchmentioning

confidence: 99%

“…Researchers had used CNN and its extensions, fully connected layer and UNet, for liver and tumor segmentation [10]- [15]. As our knowledge up to now, maximum dice score obtained for liver and tumor segmentation are 0.9522 and 0.63 respectively.…”

Section: Introductionmentioning

confidence: 97%

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Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

Ayalew

Fante

Aliy

2020

Preprint

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Background: Liver cancer is the sixth most common cancer worldwide. According to WHO data in 2017, the liver cancer death in Ethiopia reached 1040 (0.16%) from all cancer deaths. Hepatocellular carcinoma (HCC), primary liver cancer causes the death of around 700,000 people each year worldwide and this makes it the third leading cause of cancer death. HCC is occurred due to cirrhosis and hepatitis B or C viruses. Liver cancer mostly diagnosed with a computed tomography (CT) scan. But, the detection of the tumor from the CT scan image is difficult since tumors have similar intensity with nearby tissues and may have a different appearance depending on their type, state, and equipment setting. Nowadays deep learning methods have been used for the segmentation of liver and its tumor from the CT scan images and they are more efficient than those traditional methods. But, they are computationally expensive and need many labeled samples for training, which are difficult in the case of biomedical images. Results: A deep learning-based segmentation algorithm is employed for liver and tumor segmentation from abdominal CT scan images. Three separate UNet models, one for liver segmentation and the others two for tumor segmentation from the segmented liver and directly from the abdominal CT scan image were used. A dice score of 0.96 was obtained for liver segmentation. And a dice score of 0.74 and 0.63 was obtained for segmentation of tumor from the liver and from abdominal CT scan image respectively. Conclusion: The research improves the liver tumor segmentation that will help the physicians in the diagnosis and detection of liver tumors and in designing a treatment plan for the patient. And for the patient, it increases the patients’ chance of getting treatment and decrease the mortality rate due to liver cancer.

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Section: A Test Results For Liver Segmentationmentioning

confidence: 99%

Section: Related Work In the Area Of The Researchmentioning

confidence: 99%

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Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

Ayalew

Fante

Aliy

2020

Preprint

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“…Patrick et al [2] presented a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of large-scale medical trials and quantitative image analysis. Zhe Liu et al [5] proposed liver sequence CT image segmentation solution GIU-Net, which consolidates an improved U-Net and a graph cutting algorithm, to take care of the low contrast between a liver and its surrounding organs issue. The problem of the large difference among individual livers in CT image was also addressed in [5].…”

Section: Introductionmentioning

confidence: 99%

“…Zhe Liu et al [5] proposed liver sequence CT image segmentation solution GIU-Net, which consolidates an improved U-Net and a graph cutting algorithm, to take care of the low contrast between a liver and its surrounding organs issue. The problem of the large difference among individual livers in CT image was also addressed in [5].…”

Section: Introductionmentioning

confidence: 99%

Liver Segmentation from Multimodal Images Using HED-Mask R-CNN

Mulay¹,

Deepika²,

Jeevakala³

et al. 2019

Multiscale Multimodal Medical Imaging

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Precise segmentation of the liver is critical for computeraided diagnosis such as pre-evaluation of the liver for living donor-based transplantation surgery. This task is challenging due to the weak boundaries of organs, countless anatomical variations, and the complexity of the background. Computed tomography (CT) scanning and magnetic resonance imaging (MRI) images have different parameters and settings. Thus, images acquired from different modalities differ from one another making liver segmentation challenging task. We propose an efficient liver segmentation with the combination of holistically-nested edge detection (HED) and Mask-region-convolutional neural network (R-CNN) to address these challenges. The proposed HED-Mask R-CNN approach is based on effective identification of edge map from multimodal images. The proposed system firstly applies a preprocessing step of image enhancement to get the 'primal sketches' of the abdomen. Then the HED network is applied to enhanced CT and MRI modality images to get better edge map. Finally, the Mask R-CNN is used to segment the liver from edge map images. We used a dataset of 20 CT patients and 9 MR patient from the CHAOS challenge. The system is trained on CT and MRI images separately and then converted to 2D slices. We significantly improved the segmentation accuracy of CT and MRI images on a database with Dice value of 0.94 for CT, 0.89 for T2-weighted MRI and 0.91 for T1-weighted MRI.

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An integrated feature frame work for automated segmentation of COVID‐19 infection from lung CT images

Selvaraj

Venkatesan

Mahesh

et al. 2020

Int J Imaging Syst Tech

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The novel coronavirus disease (SARS‐CoV‐2 or COVID‐19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID‐19 detection. However, lung infection by COVID‐19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID‐19 region on the lungs using computed tomography (CT) scan images. To address the dataset problem, we propose a deep neural network (DNN) model trained on a limited dataset where features are selected using a region‐specific approach. Specifically, we apply the Zernike moment (ZM) and gray level co‐occurrence matrix (GLCM) to extract the unique shape and texture features. The feature vectors computed from these techniques enable segmentation that illustrates the severity of the COVID‐19 infection. The proposed algorithm was compared with other existing state‐of‐the‐art deep neural networks using the Radiopedia and COVID‐19 CT Segmentation datasets presented specificity, sensitivity, sensitivity, mean absolute error (MAE), enhance‐alignment measure (EM φ ), and structure measure ( S m ) of 0.942, 0.701, 0.082, 0.867, and 0.783, respectively. The metrics demonstrate the performance of the model in quantifying the COVID‐19 infection with limited datasets.

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Liver CT sequence segmentation based with improved U-Net and graph cut

Cited by 126 publications

References 14 publications

Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

Liver Segmentation from Multimodal Images Using HED-Mask R-CNN

An integrated feature frame work for automated segmentation of COVID‐19 infection from lung CT images

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