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Gong Z, Song J, Guo W, Ju R, Zhao D, Tan W, Zhou W, Zhang G. Abdomen tissues segmentation from computed tomography images using deep learning and level set methods. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:14074-14085. [PMID: 36654080 DOI: 10.3934/mbe.2022655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Accurate abdomen tissues segmentation is one of the crucial tasks in radiation therapy planning of related diseases. However, abdomen tissues segmentation (liver, kidney) is difficult because the low contrast between abdomen tissues and their surrounding organs. In this paper, an attention-based deep learning method for automated abdomen tissues segmentation is proposed. In our method, image cropping is first applied to the original images. U-net model with attention mechanism is then constructed to obtain the initial abdomen tissues. Finally, level set evolution which consists of three energy terms is used for optimize the initial abdomen segmentation. The proposed model is evaluated across 470 subsets. For liver segmentation, the mean dice are 96.2 and 95.1% for the FLARE21 datasets and the LiTS datasets, respectively. For kidney segmentation, the mean dice are 96.6 and 95.7% for the FLARE21 datasets and the LiTS datasets, respectively. Experimental evaluation exhibits that the proposed method can obtain better segmentation results than other methods.
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Affiliation(s)
- Zhaoxuan Gong
- Department of Computer Science and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110819, China
| | - Jing Song
- Department of Computer Science and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China
| | - Wei Guo
- Department of Computer Science and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110819, China
| | - Ronghui Ju
- Liaoning provincial people's hospital, Shenyang 110067, China
| | - Dazhe Zhao
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110819, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110819, China
| | - Wei Zhou
- Department of Computer Science and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China
| | - Guodong Zhang
- Department of Computer Science and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110819, China
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Wang X, Zhang Z, Wu K, Yin X, Guo H. Gabor Dictionary of Sparse Image Patches Selected in Prior Boundaries for 3D Liver Segmentation in CT Images. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5552864. [PMID: 34925736 PMCID: PMC8677387 DOI: 10.1155/2021/5552864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 06/26/2021] [Accepted: 10/04/2021] [Indexed: 11/17/2022]
Abstract
The gray contrast between the liver and other soft tissues is low, and the boundary is not obvious. As a result, it is still a challenging task to accurately segment the liver from CT images. In recent years, methods of machine learning have become a research hotspot in the field of medical image segmentation because they can effectively use the "gold standard" personalized features of the liver from different data. However, machine learning usually requires a large number of data samples to train the model and improve the accuracy of medical image segmentation. This paper proposed a method for liver segmentation based on the Gabor dictionary of sparse image blocks with prior boundaries. This method reduced the number of samples by selecting the test sample set within the initial boundary area of the liver. The Gabor feature was extracted and the query dictionary was created, and the sparse coefficient was calculated to obtain the boundary information of the liver. By optimizing the reconstruction error and filling holes, a smooth liver boundary was obtained. The proposed method was tested on the MICCAI 2007 dataset and ISBI2017 dataset, and five measures were used to evaluate the results. The proposed method was compared with methods for liver segmentation proposed in recent years. The experimental results show that this method can improve the accuracy of liver segmentation and effectively repair the discontinuity and local overlap of segmentation results.
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Affiliation(s)
- Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
- Research Center of Machine Vision Engineering and Technology of Hebei Province, Baoding 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071002, China
| | - Zhiling Zhang
- College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
- Research Center of Machine Vision Engineering and Technology of Hebei Province, Baoding 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071002, China
| | - Kunlun Wu
- Hebei Research Institute of Construction and Geotechnical Investigation Co.,Ltd., Shijiazhuang, Hebei, China
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University, Baoding 071000, China
| | - Haifeng Guo
- College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
- Research Center of Machine Vision Engineering and Technology of Hebei Province, Baoding 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071002, China
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Spieler B, Sabottke C, Moawad AW, Gabr AM, Bashir MR, Do RKG, Yaghmai V, Rozenberg R, Gerena M, Yacoub J, Elsayes KM. Artificial intelligence in assessment of hepatocellular carcinoma treatment response. Abdom Radiol (NY) 2021; 46:3660-3671. [PMID: 33786653 DOI: 10.1007/s00261-021-03056-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/03/2021] [Accepted: 03/09/2021] [Indexed: 02/08/2023]
Abstract
Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).
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Ren S, Zhan L, Chen S, Dai H, Ruan G, Li S, Liu L, Lin R, Chen H. Segmentation and Registration of the Liver in Dynamic Contrast-Enhanced Computed Tomography Images. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Dynamic contrast-enhanced computed tomography (DCE-CT) is the main auxiliary diagnostic tool for liver diseases. Liver segmentation and registration in all stages of DCE-CT images are the key technology for big data analysis of liver disease diagnosis. The change of imaging conditions
in different stages of DCE-CT brings enormous challenges to the segmentation of liver CT images. This study proposes an automatic model for liver segmentation from abdominal CT images in different stages of DCE on the basis of U-Net. The skip connection in U-Net can improve the ability of
complex feature recognition. A total of 4863 CT slices from 16 patients with hepatocellular carcinoma (HCC) were selected as the training set, and 1754 CT slices from 6 patients with HCC were selected as the test set. The training and test sets included plain scan, hepatic arterial-dominant
phase, and portal venous-dominant phase CT scans. Results showed that the Dice value of the proposed method was significantly higher than those of the full convolutional network and region-growing method. Then, 3D reconstruction and registration were performed on the segmentation results of
the liver region of DCE-CT images. The proposed method obtained the best performance, which can provide technical support for the big data analysis of liver diseases.
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Affiliation(s)
- Shuai Ren
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Ling Zhan
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Shuchao Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Haitao Dai
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
| | - Guangying Ruan
- State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Sai Li
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Lizhi Liu
- State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Run Lin
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
| | - Hongbo Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
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Akhtar Y, Dakua SP, Abdalla A, Aboumarzouk OM, Ansari MY, Abinahed J, Elakkad MSM, Al-Ansari A. Risk Assessment of Computer-aided Diagnostic Software for Hepatic Resection. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2021.3071148] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yusuf Akhtar
- Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India
| | | | | | | | | | - Julien Abinahed
- Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
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Fully Automatic Segmentation and Three-Dimensional Reconstruction of the Liver in CT Images. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:6797102. [PMID: 30581550 PMCID: PMC6276449 DOI: 10.1155/2018/6797102] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 09/30/2018] [Accepted: 10/22/2018] [Indexed: 11/18/2022]
Abstract
Automatic segmentation and three-dimensional reconstruction of the liver is important for liver disease diagnosis and surgical treatment. However, the shape of the imaged 2D liver in each CT image changes dramatically across the slices. In all slices, the imaged 2D liver is connected with other organs, and the connected organs also vary across the slices. In many slices, the intensities of the connected organs are the same with that of the liver. All these facts make automatic segmentation of the liver in the CT image an extremely difficult task. In this paper, we propose a heuristic approach to segment the liver automatically based on multiple thresholds. The thresholds are computed based on the slope difference distribution that has been proposed and verified in the previous research. Different organs in the CT image are segmented with the automatically computed thresholds, respectively. Then, different segmentation results are combined to delineate the boundary of the liver robustly. After the boundaries of the 2D liver in all the slices are identified, they are combined to form the 3D shape of the liver with a global energy minimization function. Experimental results verified the effectiveness of all the proposed image processing algorithms in automatic and robust segmentation of the liver in CT images.
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Li X, Chen H, Qi X, Dou Q, Fu CW, Heng PA. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2663-2674. [PMID: 29994201 DOI: 10.1109/tmi.2018.2845918] [Citation(s) in RCA: 733] [Impact Index Per Article: 122.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
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Zheng Y, He L, Yang H, Bai Y, Xie F, Kang K, Wang X. Soft-tissue-segmentation methods during image-guided precision liver surgery. Gastroenterol Rep (Oxf) 2018; 6:239-241. [PMID: 30151209 PMCID: PMC6101613 DOI: 10.1093/gastro/goy028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, P.R. China
| | - Li He
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Huayu Yang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, P.R. China
| | - Yi Bai
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, P.R. China
| | - Fucun Xie
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, P.R. China
| | - Kai Kang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, P.R. China
| | - Xuehu Wang
- School of Electronic and Information Engineering, Hebei University, Baoding, Hebei, P.R. China
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