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Horkaew P, Chansangrat J, Keeratibharat N, Le DC. Recent advances in computerized imaging and its vital roles in liver disease diagnosis, preoperative planning, and interventional liver surgery: A review. World J Gastrointest Surg 2023; 15:2382-2397. [PMID: 38111769 PMCID: PMC10725533 DOI: 10.4240/wjgs.v15.i11.2382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/30/2023] [Accepted: 09/27/2023] [Indexed: 11/26/2023] Open
Abstract
The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes. In clinical settings, screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering appropriate therapeutic procedures. Moreover, in a patient undergoing liver resection, a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments, making surgical decisions during the procedure, and anticipating postoperative results. Conventionally, various medical imaging modalities, e.g., computed tomography, magnetic resonance imaging, and positron emission tomography, have been employed to assist in these tasks. In fact, several standardized procedures, such as lesion detection and liver segmentation, are also incorporated into prominent commercial software packages. Thus far, most integrated software as a medical device typically involves tedious interactions from the physician, such as manual delineation and empirical adjustments, as per a given patient. With the rapid progress in digital health approaches, especially medical image analysis, a wide range of computer algorithms have been proposed to facilitate those procedures. They include pattern recognition of a liver, its periphery, and lesion, as well as pre- and postoperative simulations. Prior to clinical adoption, however, software must conform to regulatory requirements set by the governing agency, for instance, valid clinical association and analytical and clinical validation. Therefore, this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses, visualization, and simulation in the literature. Emphasis is placed upon their concepts, algorithmic classifications, merits, limitations, clinical considerations, and future research trends.
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Affiliation(s)
- Paramate Horkaew
- School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Jirapa Chansangrat
- School of Radiology, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Nattawut Keeratibharat
- School of Surgery, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Doan Cong Le
- Faculty of Information Technology, An Giang University, Vietnam National University (Ho Chi Minh City), An Giang 90000, Vietnam
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Yang X, Park S, Lee S, Han K, Lee MR, Song JS, Yu HC, Do Yang J. Estimation of right lobe graft weight for living donor liver transplantation using deep learning-based fully automatic computed tomographic volumetry. Sci Rep 2023; 13:17746. [PMID: 37853228 PMCID: PMC10584880 DOI: 10.1038/s41598-023-45140-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/16/2023] [Indexed: 10/20/2023] Open
Abstract
This study aimed at developing a fully automatic technique for right lobe graft weight estimation using deep learning algorithms. The proposed method consists of segmentation of the full liver region from computed tomography (CT) images, classification of the entire liver region into the right and left lobes, and estimation of the right lobe graft weight from the CT-measured right lobe graft volume using a volume-to-weight conversion formula. The first two steps were performed with a transformer-based deep learning model. To train and evaluate the model, a total of 248 CT datasets (188 for training, 40 for validation, and 20 for testing and clinical evaluation) were used. The Dice similarity coefficient (DSC), mean surface distance (MSD), and the 95th percentile Hausdorff distance (HD95) were used for evaluating the segmentation accuracy of the full liver region and the right liver lobe. The correlation coefficient (CC), percentage error (PE), and percentage absolute error (PAE) were used for the clinical evaluation of the estimated right lobe graft weight. The proposed method achieved high accuracy in segmentation for DSC, MSD, and HD95 (95.9% ± 1.0%, 1.2 ± 0.4 mm, and 5.2 ± 1.9 mm for the entire liver region; 92.4% ± 2.7%, 2.0 ± 0.7 mm, and 8.8 ± 2.9 mm for the right lobe) and in clinical evaluation for CC, PE, and PAE (0.859, - 1.8% ± 9.6%, and 8.6% ± 4.7%). For the right lobe graft weight estimation, the present study underestimated the graft weight by - 1.8% on average. A mean difference of - 21.3 g (95% confidence interval: - 55.7 to 13.1, p = 0.211) between the estimated graft weight and the actual graft weight was achieved in this study. The proposed method is effective for clinical application.
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Affiliation(s)
- Xiaopeng Yang
- School of Global Entrepreneurship and Information Communication Technology, Handong Global University, Pohang, 37554, Republic of Korea
| | - Seonyeong Park
- School of Global Entrepreneurship and Information Communication Technology, Handong Global University, Pohang, 37554, Republic of Korea
| | - Seungyoo Lee
- School of Global Entrepreneurship and Information Communication Technology, Handong Global University, Pohang, 37554, Republic of Korea
| | - Kyujin Han
- School of Global Entrepreneurship and Information Communication Technology, Handong Global University, Pohang, 37554, Republic of Korea
| | - Mi Rin Lee
- Department of Surgery, Jeonbuk National University Medical School and Hospital, Jeonju, 54907, Republic of Korea
- Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju, 54907, Republic of Korea
- Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju, 54907, Republic of Korea
| | - Ji Soo Song
- Department of Radiology, Jeonbuk National University Medical School and Hospital, Jeonju, 54907, Republic of Korea
| | - Hee Chul Yu
- Department of Surgery, Jeonbuk National University Medical School and Hospital, Jeonju, 54907, Republic of Korea
- Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju, 54907, Republic of Korea
- Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju, 54907, Republic of Korea
| | - Jae Do Yang
- Department of Surgery, Jeonbuk National University Medical School and Hospital, Jeonju, 54907, Republic of Korea.
- Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju, 54907, Republic of Korea.
- Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju, 54907, Republic of Korea.
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Wang X, Wang S, Zhang Z, Yin X, Wang T, Li N. CPAD-Net: Contextual parallel attention and dilated network for liver tumor segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Amin J, Anjum MA, Sharif M, Kadry S, Nadeem A, Ahmad SF. Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12040823. [PMID: 35453870 PMCID: PMC9025116 DOI: 10.3390/diagnostics12040823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/18/2022] [Accepted: 03/22/2022] [Indexed: 12/17/2022] Open
Abstract
Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of computed tomography (CT) for early detection of liver cancer could save millions of lives per year. There is also an urgent need for a computerized method to interpret, detect and analyze CT scans reliably, easily, and correctly. However, precise segmentation of minute tumors is a difficult task because of variation in the shape, intensity, size, low contrast of the tumor, and the adjacent tissues of the liver. To address these concerns, a model comprised of three parts: synthetic image generation, localization, and segmentation, is proposed. An optimized generative adversarial network (GAN) is utilized for generation of synthetic images. The generated images are localized by using the improved localization model, in which deep features are extracted from pre-trained Resnet-50 models and fed into a YOLOv3 detector as an input. The proposed modified model localizes and classifies the minute liver tumor with 0.99 mean average precision (mAp). The third part is segmentation, in which pre-trained Inceptionresnetv2 employed as a base-Network of Deeplabv3 and subsequently is trained on fine-tuned parameters with annotated ground masks. The experiments reflect that the proposed approach has achieved greater than 95% accuracy in the testing phase and it is proven that, in comparison to the recently published work in this domain, this research has localized and segmented the liver and minute liver tumor with more accuracy.
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Affiliation(s)
- Javaria Amin
- Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan;
| | | | - Muhammad Sharif
- Department of Computer Science, Comsats University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan;
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4609 Kristiansand, Norway
- Correspondence:
| | - Ahmed Nadeem
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; (A.N.); (S.F.A.)
| | - Sheikh F. Ahmad
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; (A.N.); (S.F.A.)
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He R, Xu S, Liu Y, Li Q, Liu Y, Zhao N, Yuan Y, Zhang H. Three-Dimensional Liver Image Segmentation Using Generative Adversarial Networks Based on Feature Restoration. Front Med (Lausanne) 2022; 8:794969. [PMID: 35071275 PMCID: PMC8777029 DOI: 10.3389/fmed.2021.794969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 11/22/2021] [Indexed: 11/13/2022] Open
Abstract
Medical imaging provides a powerful tool for medical diagnosis. In the process of computer-aided diagnosis and treatment of liver cancer based on medical imaging, accurate segmentation of liver region from abdominal CT images is an important step. However, due to defects of liver tissue and limitations of CT imaging procession, the gray level of liver region in CT image is heterogeneous, and the boundary between the liver and those of adjacent tissues and organs is blurred, which makes the liver segmentation an extremely difficult task. In this study, aiming at solving the problem of low segmentation accuracy of the original 3D U-Net network, an improved network based on the three-dimensional (3D) U-Net, is proposed. Moreover, in order to solve the problem of insufficient training data caused by the difficulty of acquiring labeled 3D data, an improved 3D U-Net network is embedded into the framework of generative adversarial networks (GAN), which establishes a semi-supervised 3D liver segmentation optimization algorithm. Finally, considering the problem of poor quality of 3D abdominal fake images generated by utilizing random noise as input, deep convolutional neural networks (DCNN) based on feature restoration method is designed to generate more realistic fake images. By testing the proposed algorithm on the LiTS-2017 and KiTS19 dataset, experimental results show that the proposed semi-supervised 3D liver segmentation method can greatly improve the segmentation performance of liver, with a Dice score of 0.9424 outperforming other methods.
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Affiliation(s)
- Runnan He
- Peng Cheng Laboratory, Shenzhen, China
| | - Shiqi Xu
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Yashu Liu
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Qince Li
- Peng Cheng Laboratory, Shenzhen, China
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Yang Liu
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Na Zhao
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Yongfeng Yuan
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Henggui Zhang
- Peng Cheng Laboratory, Shenzhen, China
- School of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
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Tang P, Zhao YQ, Liao M. Automatic multi-organ segmentation from abdominal CT volumes with LLE-based graph partitioning and 3D Chan-Vese model. Comput Biol Med 2021; 139:105030. [PMID: 34800809 DOI: 10.1016/j.compbiomed.2021.105030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/09/2021] [Accepted: 11/10/2021] [Indexed: 11/16/2022]
Abstract
This paper presents a fully automatic method for multi-organ segmentation from 3D abdominal CT volumes. Firstly, spines and ribs are removed by exponential transformation and binarization to reduce the disturbance to subsequent segmentation. Then, a Local Linear Embedding (LLE)-based graph partitioning approach is employed to perform initial segmentation for liver, spleen, and bilateral kidneys simultaneously, and a novel segmentation refinement scheme is applied composed of hybrid intensity model, 3D Chan-Vese model, and histogram equalization-based organ separation algorithm. Finally, a pseudo-3D bottleneck detection algorithm is introduced for boundary correction. The proposed method does not require heavy training or registration process and is capable of dealing with shape and location variations as well as the weak boundaries of target organs. Experiments on XHCSU20 database show the proposed method is competitive with state-of-the-art methods with Dice similarity coefficients of 95.9%, 95.1%, 94.7%, and 94.5%, Jaccard indices of 92.2%, 90.8%, 90.0%, and 89.5%, and average symmetric surface distances of 1.1 mm, 1.0 mm, 0.9 mm and 1.1 mm, for liver, spleen, left and right kidneys, respectively, and the average running time is around 6 min for a CT volume. The accuracy, precision, recall, and specificity also maintain high values for each of the four organs. Moreover, experiments on SLIVER07 dataset prove its high efficiency and accuracy on liver-only segmentation.
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Affiliation(s)
- Ping Tang
- School of Automation, Central South University, Changsha, 410083, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yu-Qian Zhao
- School of Automation, Central South University, Changsha, 410083, China; Hunan Xiangjiang Artificial Intelligence Academy, Changsha, 410083, China; Hunan Engineering Research Center of High Strength Fastener Intelligent Manufacturing, Changde, 415701, China.
| | - Miao Liao
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
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Wantanajittikul K, Saiviroonporn P, Saekho S, Krittayaphong R, Viprakasit V. An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data. BMC Med Imaging 2021; 21:138. [PMID: 34583631 PMCID: PMC8477544 DOI: 10.1186/s12880-021-00669-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 09/15/2021] [Indexed: 11/14/2022] Open
Abstract
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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Affiliation(s)
- Kittichai Wantanajittikul
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
| | - Pairash Saiviroonporn
- Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.
| | - Suwit Saekho
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
| | - Rungroj Krittayaphong
- Division of Cardiology, Department of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Vip Viprakasit
- Haematology/Oncology Division, Department of Pediatrics and Thalassemia Center, Siriraj Hospital, Mahidol University, Bangkok, Thailand
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Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy. Diagnostics (Basel) 2021; 11:diagnostics11050852. [PMID: 34068516 PMCID: PMC8151903 DOI: 10.3390/diagnostics11050852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/03/2021] [Accepted: 05/08/2021] [Indexed: 02/02/2023] Open
Abstract
Accurate localization and analyses of functional liver segments are crucial in devising various surgical procedures, including hepatectomy. To this end, they require the extraction of a liver from computed tomography, and then the identification of resection correspondence between individuals. The first part is usually impeded by inherent deficiencies, as present in medical images, and vast anatomical variations across subjects. While the model-based approach is found viable to tackle both issues, it is often undermined by an inadequate number of labeled samples, to capture all plausible variations. To address segmentation problems by balancing between accuracy, resource consumption, and data availability, this paper presents an efficient method for liver segmentation based on a graph-cut algorithm. One of its main novelties is the incorporation of a feature preserving a metric for boundary separation. Intuitive anatomical constraints are imposed to ensure valid extraction. The second part involves the symmetric conformal parameterization of the extracted liver surface onto a genus-0 domain. Provided with a few landmarks specified on two livers, we demonstrated that, by using a modified Beltrami differential, not only could they be non-rigidly registered, but also the hepatectomy on one liver could be envisioned on another. The merits of the proposed scheme were elucidated by both visual and numerical assessments on a standard MICCAI SLIVER07 dataset.
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Semi-automatic liver segmentation based on probabilistic models and anatomical constraints. Sci Rep 2021; 11:6106. [PMID: 33731736 PMCID: PMC7969941 DOI: 10.1038/s41598-021-85436-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 03/01/2021] [Indexed: 12/12/2022] Open
Abstract
Segmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.
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Li Y, Zhao YQ, Zhang F, Liao M, Yu LL, Chen BF, Wang YJ. Liver segmentation from abdominal CT volumes based on level set and sparse shape composition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105533. [PMID: 32502932 DOI: 10.1016/j.cmpb.2020.105533] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 04/22/2020] [Accepted: 05/06/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Liver segmentation from abdominal CT volumes is a primary step for computer-aided surgery and liver disease diagnosis. However, accurate liver segmentation remains a challenging task for intensity inhomogeneity and serious pathologies occurring in liver CT volume. This paper presents a novel framework for accurate liver segmentation from CT images. METHODS Firstly, a novel level set integrated with intensity bias and position constraint is applied, and for normal liver, the generated liver regions are regarded as the final results. Then, for pathological liver, a sparse shape composition (SSC)-based method is presented to refine liver shapes, followed by an improved graph cut to further optimize segmentation results. The level set-based method is capable of overcoming intensity inhomogeneity in object regions, and the SSC- and graph cut-based strategy has outstanding power to address under-segmentation appearing in pathological livers. RESULTS The experiments conducted on public databases SLIVER07 and 3Dircadb show that the proposed method can segment both healthy and pathological liver effectively. The segmentation performance in terms of mean ASD, RMSD, MSD, VOE and RVD on SLIVER07 are 0.9mm, 1.8mm, 19.4mm, 5.1% and 0.1%, respectively, and on 3Dircadb are 1.6mm, 3.1mm, 27.2mm, 9.2% and 0.5%, respectively, which outperforms many existing methods. CONCLUSIONS The proposed method does not require complex training procedure on numerous liver samples, and has satisfying and robust segmentation performance on both normal and pathological liver in various shapes.
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Affiliation(s)
- Yang Li
- School of Automation, Central South University, Changsha 410083, China; School of Computer Science and Engineering, Changsha 410083, China; Hunan Engineering Research Center of High Strength Fastener Intelligent Manufacturing, Changde 415701, China
| | - Yu-Qian Zhao
- School of Automation, Central South University, Changsha 410083, China; School of Computer Science and Engineering, Changsha 410083, China; Hunan Engineering Research Center of High Strength Fastener Intelligent Manufacturing, Changde 415701, China; DeepBlue Technology (Shanghai) Co., Ltd, Shanghai, 200042.
| | - Fan Zhang
- School of Automation, Central South University, Changsha 410083, China; Hunan Engineering Research Center of High Strength Fastener Intelligent Manufacturing, Changde 415701, China
| | - Miao Liao
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Ling-Li Yu
- School of Automation, Central South University, Changsha 410083, China; Hunan Engineering Research Center of High Strength Fastener Intelligent Manufacturing, Changde 415701, China
| | - Bai-Fan Chen
- School of Automation, Central South University, Changsha 410083, China
| | - Yan-Jin Wang
- School of Xiangya Hospital, Central South University, Changsha 410075, China.
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Masokano IB, Liu W, Xie S, Marcellin DFH, Pei Y, Li W. The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges. Cancer Imaging 2020; 20:67. [PMID: 32962762 PMCID: PMC7510095 DOI: 10.1186/s40644-020-00341-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 08/31/2020] [Indexed: 02/06/2023] Open
Abstract
Recently, radiomic texture quantification of tumors has received much attention from radiologists, scientists, and stakeholders because several results have shown the feasibility of using the technique to diagnose and manage oncological conditions. In patients with hepatocellular carcinoma, radiomics has been applied in all stages of tumor evaluation, including diagnosis and characterization of the genotypic behavior of the tumor, monitoring of treatment responses and prediction of various clinical endpoints. It is also useful in selecting suitable candidates for specific treatment strategies. However, the clinical validation of hepatocellular carcinoma radiomics is limited by challenges in imaging protocol and data acquisition parameters, challenges in segmentation techniques, dimensionality reduction, and modeling methods. Identification of the best segmentation and optimal modeling methods, as well as texture features most stable to imaging protocol variability would go a long way in harmonizing HCC radiomics for personalized patient care. This article reviews the process of HCC radiomics, its clinical applications, associated challenges, and current optimization strategies.
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Affiliation(s)
- Ismail Bilal Masokano
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Wenguang Liu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Simin Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | | | - Yigang Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
| | - Wenzheng Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
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Lee JS, Do JG, Yoon KJ, Chae SW, Park HJ, Park CH, Lee YT. Voxel-based Three-dimensional Segmentation of the Capsulo-synovium from Contrast-enhanced MRI Can Represent Clinical Impairments in Adhesive Capsulitis. Sci Rep 2020; 10:6516. [PMID: 32300141 PMCID: PMC7162880 DOI: 10.1038/s41598-020-63406-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 03/30/2020] [Indexed: 11/21/2022] Open
Abstract
The purposes were to calculate total voxel volume of the entire capsulo-synovial enhanced portion on contrast-enhanced (CE) MRI in adhesive capsulitis, and to investigate its association with glenohumeral joint volume and passive range of motions (ROMs), which are a well-known diagnostic reference standard and clinical hallmark of this condition. Medical records of 169 consecutive patients who underwent ultrasound-guided intraarticular injection with adhesive capsulitis and CE-MRI to exclude other mimicking shoulder diseases were retrospectively reviewed. To calculate total voxel volume of entire capsulo-synovial enhanced portion on CE-MRI, voxel-based 3-dimensional (3D) segmentation was obtained semi-automatically using Fiji, an open-source image processing software. Pearson’s correlation coefficients were analyzed. Sixty patients who met eligibility criteria were included. Total voxel volume showed a significant inverse correlation with the glenohumeral joint volume (r = −0.528, P < 0.001), forward elevation, external rotation, and abduction (r = −0.407, P = 0.001; r = −0.342, P = 0.007; r = −0.275, P = 0.034, respectively). Intra-observer and inter-observer reliabilities, measured by intraclass correlation coefficients (ICC), were excellent (ICC = 0.87 and 0.77, respectively). This study’s results indicate that voxel-based 3D segmentation of entire capsulo-synovial enhanced portion from CE-MRI can represent the severity of clinical impairments, such as obliterated joint volume and limited passive ROMs in adhesive capsulitis.
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Affiliation(s)
- Jung-Sang Lee
- Department of Physical and Rehabilitation Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jong Geol Do
- Department of Physical and Rehabilitation Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyung Jae Yoon
- Department of Physical and Rehabilitation Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seoung Wan Chae
- Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hee-Jin Park
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Chul-Hyun Park
- Department of Physical and Rehabilitation Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yong-Taek Lee
- Department of Physical and Rehabilitation Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Use of Spectral Detector Computed Tomography to Improve Liver Segmentation and Volumetry. J Comput Assist Tomogr 2020; 44:197-203. [PMID: 32195798 DOI: 10.1097/rct.0000000000000987] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Liver segmentation and volumetry have traditionally been performed using computed tomography (CT) attenuation to discriminate liver from other tissues. In this project, we evaluated if spectral detector CT (SDCT) can improve liver segmentation over conventional CT on 2 segmentation methods. MATERIALS AND METHODS In this Health Insurance Portability and Accountability Act-compliant institutional review board-approved retrospective study, 30 contrast-enhanced SDCT scans with healthy livers were selected. The first segmentation method is based on Gaussian mixture models of the SDCT data. The second method is a convolutional neural network-based technique called U-Net. Both methods were compared against equivalent algorithms, which used conventional CT attenuation, with hand segmentation as the reference standard. Agreement to the reference standard was assessed using Dice similarity coefficient. RESULTS Dice similarity coefficients to the reference standard are 0.93 ± 0.02 for the Gaussian mixture model method and 0.90 ± 0.04 for the CNN-based method (all 2 methods applied on SDCT). These were significantly higher compared with equivalent algorithms applied on conventional CT, with Dice coefficients of 0.90 ± 0.06 (P = 0.007) and 0.86 ± 0.06 (P < 0.001), respectively. CONCLUSION On both liver segmentation methods tested, we demonstrated higher segmentation performance when the algorithms are applied on SDCT data compared with equivalent algorithms applied on conventional CT data.
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Bi H, Jiang Y, Tang H, Yang G, Shu H, Dillenseger JL. Fast and accurate segmentation method of active shape model with Rayleigh mixture model clustering for prostate ultrasound images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 184:105097. [PMID: 31634807 DOI: 10.1016/j.cmpb.2019.105097] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 09/24/2019] [Accepted: 09/25/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE The prostate cancer interventions, which need an accurate prostate segmentation, are performed under ultrasound imaging guidance. However, prostate ultrasound segmentation is facing two challenges. The first is the low signal-to-noise ratio and inhomogeneity of the ultrasound image. The second is the non-standardized shape and size of the prostate. METHODS For prostate ultrasound image segmentation, this paper proposed an accurate and efficient method of Active shape model (ASM) with Rayleigh mixture model Clustering (ASM-RMMC). Firstly, Rayleigh mixture model (RMM) is adopted for clustering the image regions which present similar speckle distributions. These content-based clustered images are then used to initialize and guide the deformation of an ASM model. RESULTS The performance of the proposed method is assessed on 30 prostate ultrasound images using four metrics as Mean Average Distance (MAD), Dice Similarity Coefficient (DSC), False Positive Error (FPE) and False Negative Error (FNE). The proposed ASM-RMMC reaches high segmentation accuracy with 95% ± 0.81% for DSC, 1.86 ± 0.02 pixels for MAD, 2.10% ± 0.36% for FPE and 2.78% ± 0.71% for FNE, respectively. Moreover, the average segmentation time is less than 8 s when treating a single prostate ultrasound image through ASM-RMMC. CONCLUSIONS This paper presents a method for prostate ultrasound image segmentation, which achieves high accuracy with less computational complexity and meets the clinical requirements.
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Affiliation(s)
- Hui Bi
- Changzhou University, Changzhou, China
| | - Yibo Jiang
- Changzhou Institute of Technology, Changzhou, China
| | - Hui Tang
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Guanyu Yang
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China; Centre de Recherche en Information Biomédicale sino-français (CRIBs), Nanjing, China.
| | - Jean-Louis Dillenseger
- Centre de Recherche en Information Biomédicale sino-français (CRIBs), Nanjing, China; Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
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Tang W, Zou D, Yang S, Shi J, Dan J, Song G. A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab. Neural Comput Appl 2020. [DOI: 10.1007/s00521-019-04700-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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16
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Wang J, Zu H, Guo H, Bi R, Cheng Y, Tamura S. Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT. Comput Assist Surg (Abingdon) 2019; 24:20-26. [PMID: 31401890 DOI: 10.1080/24699322.2019.1649076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Abstract
Liver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing modified distance regularized level set for liver segmentation is proposed. Firstly, the similarities between training atlases and testing patient image are calculated, resulting in a series of weighted atlas, which are used to generate the patient-specific PA. Then, a most likely liver region (MLLR) can be determined based on the patient-specific PA. Finally, the refinement is performed by the modified distance regularized level set model, which takes advantage of both edge and region information as balloon force. We evaluated our proposed scheme based on 35 public datasets, and experimental result shows that the proposed method can be deployed for robust and precise liver segmentation, to replace the tedious and time-consuming manual method.
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Affiliation(s)
- Jinke Wang
- Department of Software Engineering, Harbin University of Science and Technology , Rongcheng , China.,Center for Advanced Medical Engineering and Informatics, Osaka University , Suita , Japan
| | - Hongliang Zu
- School of Computer Science and Technology, Harbin University of Science and Technology , Harbin , China
| | - Haoyan Guo
- School of Computer Science and Technology, Harbin Institute of Technology , Harbin , China
| | - Rongrong Bi
- Department of Software Engineering, Harbin University of Science and Technology , Rongcheng , China
| | - Yuanzhi Cheng
- School of Computer Science and Technology, Harbin Institute of Technology , Harbin , China
| | - Shinichi Tamura
- Center for Advanced Medical Engineering and Informatics, Osaka University , Suita , Japan
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Lebre MA, Vacavant A, Grand-Brochier M, Rositi H, Strand R, Rosier H, Abergel A, Chabrot P, Magnin B. A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities. Comput Med Imaging Graph 2019; 76:101635. [PMID: 31301489 DOI: 10.1016/j.compmedimag.2019.05.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 04/08/2019] [Accepted: 05/13/2019] [Indexed: 10/26/2022]
Abstract
Developing methods to segment the liver in medical images, study and analyze it remains a significant challenge. The shape of the liver can vary considerably from one patient to another, and adjacent organs are visualized in medical images with similar intensities, making the boundaries of the liver ambiguous. Consequently, automatic or semi-automatic segmentation of liver is a difficult task. Moreover, scanning systems and magnetic resonance imaging have different settings and parameters. Thus the images obtained differ from one machine to another. In this article, we propose an automatic model-based segmentation that allows building a faithful 3-D representation of the liver, with a mean Dice value equal to 90.3% on CT and MRI datasets. We compare our algorithm with a semi-automatic method and with other approaches according to the state of the art. Our method works with different data sources, we use a large quantity of CT and MRI images from machines in various hospitals and multiple DICOM images available from public challenges. Finally, for evaluation of liver segmentation approaches in state of the art, robustness is not adequacy addressed with a precise definition. Another originality of this article is the introduction of a novel measure of robustness, which takes into account the liver variability at different scales.
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Affiliation(s)
- Marie-Ange Lebre
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France.
| | - Antoine Vacavant
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France
| | - Manuel Grand-Brochier
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France
| | - Hugo Rositi
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France
| | - Robin Strand
- Centre for Image Analysis, Uppsala University, Sweden
| | - Hubert Rosier
- Centre Hospitalier Émile Roux, Le Puy-en-Velay, France
| | - Armand Abergel
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France
| | - Pascal Chabrot
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France
| | - Benoît Magnin
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France
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Lebre MA, Vacavant A, Grand-Brochier M, Rositi H, Abergel A, Chabrot P, Magnin B. Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme. Comput Biol Med 2019; 110:42-51. [PMID: 31121506 DOI: 10.1016/j.compbiomed.2019.04.014] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 04/18/2019] [Accepted: 04/18/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND Proper segmentation of the liver from medical images is critical for computer-assisted diagnosis, therapy and surgical planning. Knowledge of its vascular structure allows division of the liver into eight functionally independent segments, each with its own vascular inflow, known as the Couinaud scheme. Couinaud's description is the most widely used classification, since it is well-suited for surgery and accurate for the localization of lesions. However, automatic segmentation of the liver and its vascular structure to construct the Couinaud scheme remains a challenging task. METHODS We present a complete framework to obtain Couinaud's classification in three main steps; first, we propose a model-based liver segmentation, then a vascular segmentation based on a skeleton process, and finally, the construction of the eight independent liver segments. Our algorithms are automatic and allow 3D visualizations. RESULTS We validate these algorithms on various databases with different imaging modalities (Magnetic Resonance Imaging (MRI) and Computed Tomography (CT)). Experimental results are presented on diseased livers, which pose complex challenges because both the overall organ shape and the vessels can be severely deformed. A mean DICE score of 0.915 is obtained for the liver segmentation, and an average accuracy of 0.98 for the vascular network. Finally, we present an evaluation of our method for performing the Couinaud segmentation thanks to medical reports with promising results. CONCLUSIONS We were able to automatically reconstruct 3-D volumes of the liver and its vessels on MRI and CT scans. Our goal is to develop an improved method to help radiologists with tumor localization.
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Affiliation(s)
- Marie-Ange Lebre
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France.
| | - Antoine Vacavant
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Manuel Grand-Brochier
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Hugo Rositi
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Armand Abergel
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Pascal Chabrot
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Benoît Magnin
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
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Quantitative Analysis of CT Images in Patients with Pyrrolizidine Alkaloid-Induced Sinusoidal Obstruction Syndrome. Sci Rep 2019; 9:2179. [PMID: 30778132 PMCID: PMC6379399 DOI: 10.1038/s41598-019-38669-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 01/04/2019] [Indexed: 12/28/2022] Open
Abstract
This study evaluated hepatic lesion volumes on contrast-enhanced computed tomography (CT) images in patients with pyrrolizidine alkaloid-induced sinusoidal obstruction syndrome (PA-SOS) and the association of lesion volume with the clinical severity and prognosis of the disease. Twenty-five patients with PA-SOS were included in this study, and all patients were subjected to a complete CT imaging series. The imaging results were quantitatively analyzed by a threshold-based region growing algorithm. The liver volumes and hepatic lesion volumes of the patients were estimated. Based on clinical presentations, PA-SOS was classified into three categories: mild, moderate and severe. The associations of hepatic lesion volumes with liver function test parameters and the clinical severity and prognosis of the disease were analyzed. Based on estimations using the threshold-based region growing algorithm, hepatic lesion volumes in patients with mild PA-SOS were significantly lower than those in patients with moderate and severe PA-SOS (P < 0.05). The ratio of hepatic lesion volume to liver volume (Ratio) varied based on alanine aminotransferase (ALT), aspartate aminotransferase (AST) and serum total bilirubine levels; clinical severity; and disease prognosis, and the differences were statistically significant (P < 0.05). In conclusion, the threshold-based region growing algorithm can be employed to quantitatively analyze enhanced CT images of PA-SOS patients. And the ratio of hepatic lesion volume to liver volume in patients with PA-SOS is associated with a more serious clinical course and a poorer outcome.
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A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images. BIOMED RESEARCH INTERNATIONAL 2018; 2018:3815346. [PMID: 30159326 PMCID: PMC6106976 DOI: 10.1155/2018/3815346] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2018] [Revised: 07/17/2018] [Accepted: 07/26/2018] [Indexed: 12/03/2022]
Abstract
Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis. In this paper, we present a method for liver segmentation and a method for liver tumor segmentation. The two methods are grounded on a novel unified level set method (LSM), which incorporates both region information and edge information to evolve the contour. This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation. Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image. Then with manual setting of a few seed points on the obtained binary image, the following region-growing is performed to extract a rough liver region with no leakage. The unified LSM is proposed at last to refine the segmentation result. For liver tumor segmentation, a local intensity clustering based LSM coupled with hidden Markov random field and expectation-maximization (HMRF-EM) algorithm is applied to construct an enhanced edge indicator for the unified LSM. With this development, expected segmentation results can be obtained via the unified LSM, even for complex tumors. The two methods were evaluated with various datasets containing a local hospital dataset, the public datasets SLIVER07, 3Dircadb, and MIDAS via five measures. The proposed liver segmentation method outperformed other previous semiautomatic methods on the SLIVER07 dataset and required less interaction. The proposed liver tumor segmentation method was also competitive with other state-of-the-art methods in both accuracy and efficiency on the 3Dircadb database. Our methods are evaluated to be accurate and efficient, which allows their adoptions in clinical practice.
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Yang X, Yang JD, Lee S, Hwang HP, Ahn S, Yu HC, You H. Estimation of Standard Liver Volume Using CT Volume, Body Composition, and Abdominal Geometry Measurements. Yonsei Med J 2018; 59:546-553. [PMID: 29749138 PMCID: PMC5949297 DOI: 10.3349/ymj.2018.59.4.546] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 03/27/2018] [Indexed: 02/07/2023] Open
Abstract
PURPOSE The present study developed formulas for estimation of standard liver volume (SLV) with high accuracy for the Korean population. MATERIALS AND METHODS SLV estimation formulas were established using gender-balanced and gender-unbalanced measurements of anthropometric variables, body composition variables, and abdominal geometry of healthy Koreans (n=790). Total liver volume excluding blood volume, was measured based on CT volumetry. RESULTS SLV estimation formulas as preferred in various conditions of data availability were suggested in the present study. The suggested SLV estimation formulas in the present study were found superior to existing formulas, with an increased accuracy of 4.0-217.5 mL for absolute error and 0.2-18.7% for percentage of absolute error. CONCLUSION SLV estimation formulas using gender-balanced measurements showed better performance than those using gender-unbalanced measurements. Inclusion of body composition and abdominal geometry variables contributed to improved performance of SLV estimation.
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Affiliation(s)
- Xiaopeng Yang
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Jae Do Yang
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Korea
- Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, Korea
| | - Seunghoon Lee
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Hong Pil Hwang
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Korea
- Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, Korea
| | - Sungwoo Ahn
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Korea
- Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, Korea
| | - Hee Chul Yu
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Korea
- Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, Korea.
| | - Heecheon You
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Korea.
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Yang X, Yang JD, Hwang HP, Yu HC, Ahn S, Kim BW, You H. Segmentation of liver and vessels from CT images and classification of liver segments for preoperative liver surgical planning in living donor liver transplantation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:41-52. [PMID: 29544789 DOI: 10.1016/j.cmpb.2017.12.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Revised: 11/13/2017] [Accepted: 12/11/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The present study developed an effective surgical planning method consisting of a liver extraction stage, a vessel extraction stage, and a liver segment classification stage based on abdominal computerized tomography (CT) images. METHODS An automatic seed point identification method, customized level set methods, and an automated thresholding method were applied in this study to extraction of the liver, portal vein (PV), and hepatic vein (HV) from CT images. Then, a semi-automatic method was developed to separate PV and HV. Lastly, a local searching method was proposed for identification of PV branches and the nearest neighbor approximation method was applied to classifying liver segments. RESULTS Onsite evaluation of liver segmentation provided by the SLIVER07 website showed that the liver segmentation method achieved an average volumetric overlap accuracy of 95.2%. An expert radiologist evaluation of vessel segmentation showed no false positive errors or misconnections between PV and HV in the extracted vessel trees. Clinical evaluation of liver segment classification using 43 CT datasets from two medical centers showed that the proposed method achieved high accuracy in liver graft volumetry (absolute error, AE = 45.2 ± 20.9 ml; percentage of AE, %AE = 6.8% ± 3.2%; percentage of %AE > 10% = 16.3%; percentage of %AE > 20% = none) and the classified segment boundaries agreed with the intraoperative surgical cutting boundaries by visual inspection. CONCLUSIONS The method in this study is effective in segmentation of liver and vessels and classification of liver segments and can be applied to preoperative liver surgical planning in living donor liver transplantation.
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Affiliation(s)
- Xiaopeng Yang
- Department of Industrial Management and Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea
| | - Jae Do Yang
- Department of Surgery, Chonbuk National University Medical School, Jeonju, 54907, South Korea; Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, 54907, South Korea; Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, 54907, South Korea
| | - Hong Pil Hwang
- Department of Surgery, Chonbuk National University Medical School, Jeonju, 54907, South Korea; Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, 54907, South Korea; Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, 54907, South Korea
| | - Hee Chul Yu
- Department of Surgery, Chonbuk National University Medical School, Jeonju, 54907, South Korea; Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, 54907, South Korea; Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, 54907, South Korea.
| | - Sungwoo Ahn
- Department of Surgery, Chonbuk National University Medical School, Jeonju, 54907, South Korea; Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, 54907, South Korea; Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, 54907, South Korea
| | - Bong-Wan Kim
- Department of Liver Transplantation and Hepatobiliary Surgery, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Heecheon You
- Department of Industrial Management and Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea
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Yang X, Yang JD, Yu HC, Choi Y, Yang K, Lee TB, Hwang HP, Ahn S, You H. Dr. Liver: A preoperative planning system of liver graft volumetry for living donor liver transplantation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:11-19. [PMID: 29544776 DOI: 10.1016/j.cmpb.2018.01.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 01/11/2018] [Accepted: 01/24/2018] [Indexed: 05/28/2023]
Abstract
BACKGROUND AND OBJECTIVE Manual tracing of the right and left liver lobes from computed tomography (CT) images for graft volumetry in preoperative surgery planning of living donor liver transplantation (LDLT) is common at most medical centers. This study aims to develop an automatic system with advanced image processing algorithms and user-friendly interfaces for liver graft volumetry and evaluate its accuracy and efficiency in comparison with a manual tracing method. METHODS The proposed system provides a sequential procedure consisting of (1) liver segmentation, (2) blood vessel segmentation, and (3) virtual liver resection for liver graft volumetry. Automatic segmentation algorithms using histogram analysis, hybrid level-set methods, and a customized region growing method were developed. User-friendly interfaces such as sequential and hierarchical user menus, context-sensitive on-screen hotkey menus, and real-time sound and visual feedback were implemented. Blood vessels were excluded from the liver for accurate liver graft volumetry. A large sphere-based interactive method was developed for dividing the liver into left and right lobes with a customized cutting plane. The proposed system was evaluated using 50 CT datasets in terms of graft weight estimation accuracy and task completion time through comparison to the manual tracing method. The accuracy of liver graft weight estimation was assessed by absolute difference (AD) and percentage of AD (%AD) between preoperatively estimated graft weight and intraoperatively measured graft weight. Intra- and inter-observer agreements of liver graft weight estimation were assessed by intraclass correlation coefficients (ICCs) using ten cases randomly selected. RESULTS The proposed system showed significantly higher accuracy and efficiency in liver graft weight estimation (AD = 21.0 ± 18.4 g; %AD = 3.1% ± 2.8%; percentage of %AD > 10% = none; task completion time = 7.3 ± 1.4 min) than the manual tracing method (AD = 70.5 ± 52.1 g; %AD = 10.2% ± 7.5%; percentage of %AD > 10% = 46%; task completion time = 37.9 ± 7.0 min). The proposed system showed slightly higher intra- and inter-observer agreements (ICC = 0.996 to 0.998) than the manual tracing method (ICC = 0.979 to 0.999). CONCLUSIONS The proposed system was proved accurate and efficient in liver graft volumetry for preoperative planning of LDLT.
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Affiliation(s)
- Xiaopeng Yang
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Jae Do Yang
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Republic of Korea; Research Institute of Clinical Medicine of Chonbuk National University, Jeonju, Republic of Korea; Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Republic of Korea
| | - Hee Chul Yu
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Republic of Korea; Research Institute of Clinical Medicine of Chonbuk National University, Jeonju, Republic of Korea; Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Republic of Korea.
| | - Younggeun Choi
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Kwangho Yang
- Department of Surgery, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - Tae Beom Lee
- Department of Surgery, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - Hong Pil Hwang
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Republic of Korea; Research Institute of Clinical Medicine of Chonbuk National University, Jeonju, Republic of Korea; Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Republic of Korea
| | - Sungwoo Ahn
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Republic of Korea; Research Institute of Clinical Medicine of Chonbuk National University, Jeonju, Republic of Korea; Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Republic of Korea
| | - Heecheon You
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
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Quantified degree of eccentricity of aortic valve calcification predicts risk of paravalvular regurgitation and response to balloon post-dilation after self-expandable transcatheter aortic valve replacement. Int J Cardiol 2018; 259:60-68. [DOI: 10.1016/j.ijcard.2017.12.052] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2017] [Revised: 11/12/2017] [Accepted: 12/17/2017] [Indexed: 11/19/2022]
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Zhou Z, Xue-chang Z, Si-ming Z, Hua-fei X, Yue-ding S. Semi-automatic Liver Segmentation in CT Images Through Intensity Separation and Region Growing. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.procs.2018.04.206] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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Cheng DC, Chen LW, Shen YW, Fuh LJ. Computer-assisted system on mandibular canal detection. BIOMED ENG-BIOMED TE 2017; 62:575-580. [PMID: 27861137 DOI: 10.1515/bmt-2016-0088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2016] [Accepted: 09/29/2016] [Indexed: 11/15/2022]
Abstract
BACKGROUND A dentist always checks a patient by using panoramic radiography (PR) initially. The measurement of the minimal distance (MD) between the alveolar crest and the mandibular canal (MC) superior border is critically important before the dental implant surgery, extraction of 3rd molar teeth or any surgery in the posterior area of minimal distance (MD). However, the image of MC is not always clear to identify. OBJECTIVE A software is needed for training dentists as well as a tool of demonstration to patients in clinics precisely and quickly. Moreover, it should be able to calculate the MD between the alveolar crest and the MC superior border before dental implant. METHODS A computer-aided software system to semi-automatically detect the MC and mental foramen (MF) in the PR with minimal human interference is proposed. RESULTS The result shows that the averaged relative error (RE) is 1.83% with a standard deviation of 2.31%. CONCLUSION The results show that the proposed algorithm is able to detect the MC superior and inferior borders. This system has the potential to train young clinicians and to replace the manual work in measuring the MD between the alveolar crest and the MC superior border with a minimal human intervention.
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Zou Z, Liao SH, Luo SD, Liu Q, Liu SJ. Semi-automatic segmentation of femur based on harmonic barrier. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 143:171-184. [PMID: 28391815 DOI: 10.1016/j.cmpb.2017.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2016] [Revised: 02/19/2017] [Accepted: 03/01/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Segmentation of the femur from the hip joint in computed tomography (CT) is an important preliminary step in hip surgery planning and simulation. However, this is a time-consuming and challenging task due to the weak boundary, the varying topology of the hip joint, and the extremely narrow or blurred space between the femoral head and the acetabulum. To address these problems, this study proposed a semi-automatic segmentation framework based on harmonic fields for accurate segmentation. METHODS The proposed method comprises three steps. First, with high-level information provided by the user, shape information provided by neighboring slices as well as the statistical information in the mask, a region selection method is proposed to effectively locate joint space for the harmonic field. Second, incorporated with an improved gradient, the harmonic field is used to adaptively extract a curve as the barrier that separates the femoral head from the acetabulum accurately. Third, a divide and conquer segmentation strategy based on the harmonic barrier is used to combine the femoral head part and body part as the final segmentation result. RESULTS We have tested 40 hips with considerately narrow or disappeared joint spaces. The experimental results are evaluated based on Jaccard, Dice, directional cut discrepancy (DCD) and receiver operating characteristic (ROC), and we achieve the higher Jaccard of 84.02%, Dice of 85.96%, area under curve (AUC) of 89.3%, and the lower error with DCD of 0.52mm. The effective ratio of our method is 79.1% even for cases with severe malformation. The results show that our method performs best in terms of effectiveness and accuracy on the whole data set. CONCLUSIONS The proposed method is efficient to segment femurs with narrow joint space. The accurate segmentation results can assist the physicians for osteoarthritis diagnosis in future.
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Affiliation(s)
- Zheng Zou
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China.
| | - Sheng-Hui Liao
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China.
| | - San-Ding Luo
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China.
| | - Qing Liu
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China.
| | - Shi-Jian Liu
- School of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China.
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Liao M, Zhao YQ, Liu XY, Zeng YZ, Zou BJ, Wang XF, Shih FY. Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 143:1-12. [PMID: 28391807 DOI: 10.1016/j.cmpb.2017.02.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Revised: 01/24/2017] [Accepted: 02/09/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Identifying liver regions from abdominal computed tomography (CT) volumes is an important task for computer-aided liver disease diagnosis and surgical planning. This paper presents a fully automatic method for liver segmentation from CT volumes based on graph cuts and border marching. METHODS An initial slice is segmented by density peak clustering. Based on pixel- and patch-wise features, an intensity model and a PCA-based regional appearance model are developed to enhance the contrast between liver and background. Then, these models as well as the location constraint estimated iteratively are integrated into graph cuts in order to segment the liver in each slice automatically. Finally, a vessel compensation method based on the border marching is used to increase the segmentation accuracy. RESULTS Experiments are conducted on a clinical data set we created and also on the MICCAI2007 Grand Challenge liver data. The results show that the proposed intensity, appearance models, and the location constraint are significantly effective for liver recognition, and the undersegmented vessels can be compensated by the border marching based method. The segmentation performances in terms of VOE, RVD, ASD, RMSD, and MSD as well as the average running time achieved by our method on the SLIVER07 public database are 5.8 ± 3.2%, -0.1 ± 4.1%, 1.0 ± 0.5mm, 2.0 ± 1.2mm, 21.2 ± 9.3mm, and 4.7 minutes, respectively, which are superior to those of existing methods. CONCLUSIONS The proposed method does not require time-consuming training process and statistical model construction, and is capable of dealing with complicated shapes and intensity variations successfully.
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Affiliation(s)
- Miao Liao
- School of Information Science and Engineering, Central South University, Changsha 410083, China; School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Yu-Qian Zhao
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Xi-Yao Liu
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Ye-Zhan Zeng
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Bei-Ji Zou
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Xiao-Fang Wang
- Department of Mathematics and Computer Science, École centrale de Lyon, Écully, France
| | - Frank Y Shih
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA
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Mussin N, Sumo M, Lee KW, Choi Y, Choi JY, Ahn SW, Yoon KC, Kim HS, Hong SK, Yi NJ, Suh KS. The correlation between preoperative volumetry and real graft weight: comparison of two volumetry programs. Ann Surg Treat Res 2017; 92:214-220. [PMID: 28382294 PMCID: PMC5378562 DOI: 10.4174/astr.2017.92.4.214] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 11/01/2016] [Accepted: 11/01/2016] [Indexed: 12/16/2022] Open
Abstract
Purpose Liver volumetry is a vital component in living donor liver transplantation to determine an adequate graft volume that meets the metabolic demands of the recipient and at the same time ensures donor safety. Most institutions use preoperative contrast-enhanced CT image-based software programs to estimate graft volume. The objective of this study was to evaluate the accuracy of 2 liver volumetry programs (Rapidia vs. Dr. Liver) in preoperative right liver graft estimation compared with real graft weight. Methods Data from 215 consecutive right lobe living donors between October 2013 and August 2015 were retrospectively reviewed. One hundred seven patients were enrolled in Rapidia group and 108 patients were included in the Dr. Liver group. Estimated graft volumes generated by both software programs were compared with real graft weight measured during surgery, and further classified into minimal difference (≤15%) and big difference (>15%). Correlation coefficients and degree of difference were determined. Linear regressions were calculated and results depicted as scatterplots. Results Minimal difference was observed in 69.4% of cases from Dr. Liver group and big difference was seen in 44.9% of cases from Rapidia group (P = 0.035). Linear regression analysis showed positive correlation in both groups (P < 0.01). However, the correlation coefficient was better for the Dr. Liver group (R2 = 0.719), than for the Rapidia group (R2 = 0.688). Conclusion Dr. Liver can accurately predict right liver graft size better and faster than Rapidia, and can facilitate preoperative planning in living donor liver transplantation.
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Affiliation(s)
- Nadiar Mussin
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Marco Sumo
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea.; Department of Surgery, The Medical City Hospital, Manila, Philippines
| | - Kwang-Woong Lee
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - YoungRok Choi
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jin Yong Choi
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Sung-Woo Ahn
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Kyung Chul Yoon
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Hyo-Sin Kim
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Suk Kyun Hong
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Nam-Joon Yi
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Kyung-Suk Suh
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
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Yang X, Chu C, Yang J, Yang K, Yu H, Cho B, You H. Estimation of Right-Lobe Graft Weight From Computed Tomographic Volumetry for Living Donor Liver Transplantation. Transplant Proc 2017; 49:303-308. [DOI: 10.1016/j.transproceed.2016.12.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 12/13/2016] [Indexed: 02/07/2023]
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Liao M, Zhao YQ, Wang W, Zeng YZ, Yang Q, Shih FY, Zou BJ. Efficient liver segmentation in CT images based on graph cuts and bottleneck detection. Phys Med 2016; 32:1383-1396. [PMID: 27771278 DOI: 10.1016/j.ejmp.2016.10.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 10/05/2016] [Accepted: 10/05/2016] [Indexed: 12/20/2022] Open
Abstract
Liver segmentation from abdominal computed tomography (CT) volumes is extremely important for computer-aided liver disease diagnosis and surgical planning of liver transplantation. Due to ambiguous edges, tissue adhesion, and variation in liver intensity and shape across patients, accurate liver segmentation is a challenging task. In this paper, we present an efficient semi-automatic method using intensity, local context, and spatial correlation of adjacent slices for the segmentation of healthy liver regions in CT volumes. An intensity model is combined with a principal component analysis (PCA) based appearance model to exclude complex background and highlight liver region. They are then integrated with location information from neighboring slices into graph cuts to segment the liver in each slice automatically. Finally, a boundary refinement method based on bottleneck detection is used to increase the segmentation accuracy. Our method does not require heavy training process or statistical model construction, and is capable of dealing with complicated shape and intensity variations. We apply the proposed method on XHCSU14 and SLIVER07 databases, and evaluate it by MICCAI criteria and Dice similarity coefficient. Experimental results show our method outperforms several existing methods on liver segmentation.
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Affiliation(s)
- Miao Liao
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Yu-Qian Zhao
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Wei Wang
- The Third Xiangya Hospital, Central South University, Changsha 410083, China.
| | - Ye-Zhan Zeng
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Qing Yang
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Frank Y Shih
- College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Bei-Ji Zou
- School of Information Science and Engineering, Central South University, Changsha 410083, China
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Lin PL, Huang PW, Huang PY, Hsu HC. Alveolar bone-loss area localization in periodontitis radiographs based on threshold segmentation with a hybrid feature fused of intensity and the H-value of fractional Brownian motion model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 121:117-126. [PMID: 26078207 DOI: 10.1016/j.cmpb.2015.05.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 05/11/2015] [Accepted: 05/19/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Periodontitis involves progressive loss of alveolar bone around the teeth. Hence, automatic alveolar bone-loss (ABL) measurement in periapical radiographs can assist dentists in diagnosing such disease. In this paper, we propose an effective method for ABL area localization and denote it as ABLIfBm. METHOD ABLIfBm is a threshold segmentation method that uses a hybrid feature fused of both intensity and texture measured by the H-value of fractional Brownian motion (fBm) model, where the H-value is the Hurst coefficient in the expectation function of a fBm curve (intensity change) and is directly related to the value of fractal dimension. Adopting leave-one-out cross validation training and testing mechanism, ABLIfBm trains weights for both features using Bayesian classifier and transforms the radiograph image into a feature image obtained from a weighted average of both features. Finally, by Otsu's thresholding, it segments the feature image into normal and bone-loss regions. RESULTS Experimental results on 31 periodontitis radiograph images in terms of mean true positive fraction and false positive fraction are about 92.5% and 14.0%, respectively, where the ground truth is provided by a dentist. The results also demonstrate that ABLIfBm outperforms (a) the threshold segmentation method using either feature alone or a weighted average of the same two features but with weights trained differently; (b) a level set segmentation method presented earlier in literature; and (c) segmentation methods based on Bayesian, K-NN, or SVM classifier using the same two features. CONCLUSION Our results suggest that the proposed method can effectively localize alveolar bone-loss areas in periodontitis radiograph images and hence would be useful for dentists in evaluating degree of bone-loss for periodontitis patients.
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Affiliation(s)
- P L Lin
- Department of Computer Science and Information Engineering, Providence University, Shalu, Taichung 43301, Taiwan.
| | - P W Huang
- Department of Computer Science and Engineering, National Chung Hsing University, Taichung 40227, Taiwan.
| | - P Y Huang
- Department of Computer Science and Engineering, National Chung Hsing University, Taichung 40227, Taiwan.
| | - H C Hsu
- College of Oral Medicine, Chung Shan Medical University and Chung Shan Medical University Hospital, Taichung 40201, Taiwan.
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Zygomalas A, Karavias D, Koutsouris D, Maroulis I, Karavias DD, Giokas K, Megalooikonomou V. Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm. Med Biol Eng Comput 2015; 54:711-21. [PMID: 26307199 DOI: 10.1007/s11517-015-1369-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 08/07/2015] [Indexed: 02/06/2023]
Abstract
We developed a medical image segmentation and preoperative planning application which implements a semiautomatic and a hybrid semiautomatic liver segmentation algorithm. The aim of this study was to evaluate the feasibility of computer-assisted liver tumor surgery using these algorithms which are based on thresholding by pixel intensity value from initial seed points. A random sample of 12 patients undergoing elective high-risk hepatectomies at our institution was prospectively selected to undergo computer-assisted surgery using our algorithms (June 2013-July 2014). Quantitative and qualitative evaluation was performed. The average computer analysis time (segmentation, resection planning, volumetry, visualization) was 45 min/dataset. The runtime for the semiautomatic algorithm was <0.2 s/slice. Liver volumetric segmentation using the hybrid method was achieved in 12.9 s/dataset (SD ± 6.14). Mean similarity index was 96.2 % (SD ± 1.6). The future liver remnant volume calculated by the application showed a correlation of 0.99 to that calculated using manual boundary tracing. The 3D liver models and the virtual liver resections had an acceptable coincidence with the real intraoperative findings. The patient-specific 3D models produced using our semiautomatic and hybrid semiautomatic segmentation algorithms proved to be accurate for the preoperative planning in liver tumor surgery and effectively enhanced the intraoperative medical image guidance.
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Affiliation(s)
- Apollon Zygomalas
- Hepatobiliary and Pancreatic Unit, Department of Surgery, University Hospital of Patras, 26500, Patras, Greece. .,Computer Engineering and Informatics Department, School of Engineering, University of Patras, 26500, Rio, Patras, Greece.
| | - Dionissios Karavias
- Hepatobiliary and Pancreatic Unit, Department of Surgery, University Hospital of Patras, 26500, Patras, Greece
| | - Dimitrios Koutsouris
- Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zografou, Athens, Greece
| | - Ioannis Maroulis
- Hepatobiliary and Pancreatic Unit, Department of Surgery, University Hospital of Patras, 26500, Patras, Greece
| | - Dimitrios D Karavias
- Hepatobiliary and Pancreatic Unit, Department of Surgery, University Hospital of Patras, 26500, Patras, Greece
| | - Konstantinos Giokas
- Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zografou, Athens, Greece
| | - Vasileios Megalooikonomou
- Computer Engineering and Informatics Department, School of Engineering, University of Patras, 26500, Rio, Patras, Greece
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Aoki EM, Abdala-Júnior R, de Oliveira JX, Arita ES, Cortes ARG. Reliability and Reproducibility of Manual and Automated Volumetric Measurements of Periapical Lesions. J Endod 2015; 41:1555-9. [PMID: 26234544 DOI: 10.1016/j.joen.2015.06.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Revised: 06/17/2015] [Accepted: 06/20/2015] [Indexed: 10/23/2022]
Abstract
INTRODUCTION The present study aimed to test the reliability and reproducibility of 2 methods: manual and automated segmentation (using a threshold-based region growing algorithm) for measuring the volume of periapical lesions. METHODS A total of 43 cone-beam computed tomographic scans (14 men and 29 women, mean age of 54.6 ± 8.5 years) were analyzed by 3 observers. Intraobserver reproducibility and interobserver reliability were assessed using the intraclass correlation coefficient. Parametric correlation between manual and automated volumetric measurements was performed. In addition, the Student t test was also used to compare the mean time required for manual and automated volumetric measurements. RESULTS Automated segmentation showed slightly higher intraclass correlation coefficient values for all observers. A strong significant correlation was found between manual and automated volumetric measurements performed by the 3 observers. A significant difference in the mean procedure time was also found between both methods (P = .001). CONCLUSIONS Within the limitations of this study, the present results suggest that automated segmentation with a region growing algorithm is faster and slightly more reliable to calculate the volume of periapical lesions.
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Affiliation(s)
- Eduardo Massaharu Aoki
- Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, Brazil
| | - Reinaldo Abdala-Júnior
- Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, Brazil
| | | | - Emiko Saito Arita
- Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, Brazil
| | - Arthur Rodriguez Gonzalez Cortes
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts; Harvard Medical School, Boston, Massachusetts.
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Ungru K, Tenbrinck D, Jiang X, Stypmann J. Automatic classification of left ventricular wall segments in small animal ultrasound imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 117:2-12. [PMID: 25053013 DOI: 10.1016/j.cmpb.2014.06.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Revised: 06/19/2014] [Accepted: 06/20/2014] [Indexed: 06/03/2023]
Abstract
Multiple statistics show that heart diseases are one of the main causes of mortality in our highly developed societies today. These diseases lead to a change of the physiology of the heart, which gives useful information about characteristic and severity of the defect. A fast and reliable diagnosis is the base for successful therapy. As a first step towards recognition of such heart remodeling processes, this work proposes a fully automatic processing pipeline for regional classification of the left ventricular wall in ultrasound images of small animals. The pipeline is based on state-of-the-art methods from computer vision and pattern classification. The myocardial wall is segmented and its motion is estimated. A feature extraction using the segmented data is realized to automatically classify the image regions into normal and abnormal myocardial tissue. The performance of the proposed pipeline is evaluated and a comparison of common classification algorithms on ultrasound data of living mice before and after artificially induced myocardial infarction is given. It is shown that the results of this work, reaching a maximum accuracy of 91.46%, are an encouraging base for further investigation.
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Affiliation(s)
- Kathrin Ungru
- Department of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Daniel Tenbrinck
- Department of Mathematics and Computer Science, University of Münster, Münster, Germany; Department of Cardiovascular Medicine, Division of Cardiology, University Hospital Münster, Münster, Germany
| | - Xiaoyi Jiang
- Department of Mathematics and Computer Science, University of Münster, Münster, Germany; Cluster of Excellence EXC 1003, Cells in Motion (CiM), University of Münster, Münster, Germany.
| | - Jörg Stypmann
- Department of Cardiovascular Medicine, Division of Cardiology, University Hospital Münster, Münster, Germany
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Wen R, Tay WL, Nguyen BP, Chng CB, Chui CK. Hand gesture guided robot-assisted surgery based on a direct augmented reality interface. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 116:68-80. [PMID: 24438993 DOI: 10.1016/j.cmpb.2013.12.018] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2013] [Revised: 12/17/2013] [Accepted: 12/22/2013] [Indexed: 06/03/2023]
Abstract
Radiofrequency (RF) ablation is a good alternative to hepatic resection for treatment of liver tumors. However, accurate needle insertion requires precise hand-eye coordination and is also affected by the difficulty of RF needle navigation. This paper proposes a cooperative surgical robot system, guided by hand gestures and supported by an augmented reality (AR)-based surgical field, for robot-assisted percutaneous treatment. It establishes a robot-assisted natural AR guidance mechanism that incorporates the advantages of the following three aspects: AR visual guidance information, surgeon's experiences and accuracy of robotic surgery. A projector-based AR environment is directly overlaid on a patient to display preoperative and intraoperative information, while a mobile surgical robot system implements specified RF needle insertion plans. Natural hand gestures are used as an intuitive and robust method to interact with both the AR system and surgical robot. The proposed system was evaluated on a mannequin model. Experimental results demonstrated that hand gesture guidance was able to effectively guide the surgical robot, and the robot-assisted implementation was found to improve the accuracy of needle insertion. This human-robot cooperative mechanism is a promising approach for precise transcutaneous ablation therapy.
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Affiliation(s)
- Rong Wen
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576, Singapore
| | - Wei-Liang Tay
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Binh P Nguyen
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576, Singapore.
| | - Chin-Boon Chng
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576, Singapore
| | - Chee-Kong Chui
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576, Singapore
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López-Mir F, Naranjo V, Angulo J, Alcañiz M, Luna L. Liver segmentation in MRI: A fully automatic method based on stochastic partitions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 114:11-28. [PMID: 24529637 DOI: 10.1016/j.cmpb.2013.12.022] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Revised: 12/20/2013] [Accepted: 12/24/2013] [Indexed: 06/03/2023]
Abstract
There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 ± 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.
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Affiliation(s)
- F López-Mir
- Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
| | - V Naranjo
- Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
| | - J Angulo
- CMM-Centre de Morphologie Mathématique, Mathématiques et Systèmes, MINES Paristech, France
| | - M Alcañiz
- Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain; Ciber, Fisiopatología de Obesidad y Nutrición, CB06/03 Instituto de Salud Carlos III, Spain
| | - L Luna
- Hospital Clínica Benidorm (Unidad de Resonancia Magnética INSCANNER), Spain
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Selver MA. Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 113:830-852. [PMID: 24480371 DOI: 10.1016/j.cmpb.2013.12.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Revised: 11/09/2013] [Accepted: 12/17/2013] [Indexed: 06/03/2023]
Abstract
Precise measurements on abdominal organs are vital prior to the important clinical procedures. Such measurements require accurate segmentation of these organs, which is a very challenging task due to countless anatomical variations and technical difficulties. Although, several features with various classifiers have been designed to overcome these challenges, abdominal organ segmentation via classification is still an emerging field in order to reach desired precision. Recent studies on multiple feature-classifier combinations show that hierarchical systems outperform composite feature-single classifier models. In this study, how hierarchical formations can translate to improved accuracy, when large size feature spaces are involved, is explored for the problem of abdominal organ segmentation. As a result, a semi-automatic, slice-by-slice segmentation method is developed using a novel multi-level and hierarchical neural network (MHNN). MHNN is designed to collect complementary information about organs at each level of the hierarchy via different feature-classifier combinations. Moreover, each level of MHNN receives residual data from the previous level. The residual data is constructed to preserve zero false positive error until the last level of the hierarchy, where only most challenging samples remain. The algorithm mimics analysis behaviour of a radiologist by using the slice-by-slice iteration, which is supported with adjacent slice similarity features. This enables adaptive determination of system parameters and turns into the advantage of online training, which is done in parallel to the segmentation process. Proposed design can perform robust and accurate segmentation of abdominal organs as validated by using diverse data sets with various challenges.
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Affiliation(s)
- M Alper Selver
- Department of Electrical and Electronics Engineering, Dokuz Eylül University, İzmir 35160, Turkey.
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