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Huang S, Luo J, Ou Y, Shen W, Pang Y, Nie X, Zhang G. Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (CT) images. J Cancer Res Clin Oncol 2024; 150:79. [PMID: 38316678 PMCID: PMC10844439 DOI: 10.1007/s00432-023-05564-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
INTRODUCTION The automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such a learning framework is built on laborious manual annotation with strict requirements for expertise, leading to insufficient high-quality labels. METHODS To overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised double-cooperative network (SD- Net). SD-Net is trained to segment the complete liver volume from preoperative abdominal CT images by using limited labeled datasets and large-scale unlabeled datasets. Specifically, to enrich the diversity of unsupervised information, we construct SD-Net consisting of two collaborative network models. Within the supervised training module, we introduce an adaptive mask refinement approach. First, each of the two network models predicts the labeled dataset, after which adaptive mask refinement of the difference predictions is implemented to obtain more accurate liver segmentation results. In the unsupervised training module, a dynamic pseudo-label generation strategy is proposed. First each of the two models predicts unlabeled data and the better prediction is considered as pseudo-labeling before training. RESULTS AND DISCUSSION Based on the experimental findings, the proposed method achieves a dice score exceeding 94%, indicating its high level of accuracy and its suitability for everyday clinical use.
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Affiliation(s)
- Shixin Huang
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
- Department of Scientific Research, The People's Hospital of Yubei District of Chongqing city, Chongqing, 401120, China
| | - Jiawei Luo
- West China Biomedical Big Data Center, West China Hospital, Chengdu, 610044, China
| | - Yangning Ou
- School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, 201418, China
| | - Wangjun Shen
- Chongqing Human Resources Development Service Center, Chongqing, 400065, China
| | - Yu Pang
- School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Xixi Nie
- College of Computer Science and Technology, The Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Guo Zhang
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China.
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Gross M, Arora S, Huber S, Kücükkaya AS, Onofrey JA. LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis. Data Brief 2023; 51:109662. [PMID: 37869619 PMCID: PMC10587725 DOI: 10.1016/j.dib.2023.109662] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 09/20/2023] [Accepted: 10/04/2023] [Indexed: 10/24/2023] Open
Abstract
Accurate segmentation of liver and tumor regions in medical imaging is crucial for the diagnosis, treatment, and monitoring of hepatocellular carcinoma (HCC) patients. However, manual segmentation is time-consuming and subject to inter- and intra-rater variability. Therefore, automated methods are necessary but require rigorous validation of high-quality segmentations based on a consensus of raters. To address the need for reliable and comprehensive data in this domain, we present LiverHccSeg, a dataset that provides liver and tumor segmentations on multiphasic contrast-enhanced magnetic resonance imaging from two board-approved abdominal radiologists, along with an analysis of inter-rater agreement. LiverHccSeg provides a curated resource for liver and HCC tumor segmentation tasks. The dataset includes a scientific reading and co-registered contrast-enhanced multiphasic magnetic resonance imaging (MRI) scans with corresponding manual segmentations by two board-approved abdominal radiologists and relevant metadata and offers researchers a comprehensive foundation for external validation, and benchmarking of liver and tumor segmentation algorithms. The dataset also provides an analysis of the agreement between the two sets of liver and tumor segmentations. Through the calculation of appropriate segmentation metrics, we provide insights into the consistency and variability in liver and tumor segmentations among the radiologists. A total of 17 cases were included for liver segmentation and 14 cases for HCC tumor segmentation. Liver segmentations demonstrates high segmentation agreement (mean Dice, 0.95 ± 0.01 [standard deviation]) and HCC tumor segmentations showed higher variation (mean Dice, 0.85 ± 0.16 [standard deviation]). The applications of LiverHccSeg can be manifold, ranging from testing machine learning algorithms on public external data to radiomic feature analyses. Leveraging the inter-rater agreement analysis within the dataset, researchers can investigate the impact of variability on segmentation performance and explore methods to enhance the accuracy and robustness of liver and tumor segmentation algorithms in HCC patients. By making this dataset publicly available, LiverHccSeg aims to foster collaborations, facilitate innovative solutions, and ultimately improve patient outcomes in the diagnosis and treatment of HCC.
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Affiliation(s)
- Moritz Gross
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States of America
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sandeep Arora
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States of America
| | - Steffen Huber
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States of America
| | - Ahmet S. Kücükkaya
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States of America
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - John A. Onofrey
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States of America
- Department of Urology, Yale University School of Medicine, New Haven, CT, United States of America
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America
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Saumiya S, Franklin SW. Residual Deformable Split Channel and Spatial U-Net for Automated Liver and Liver Tumour Segmentation. J Digit Imaging 2023; 36:2164-2178. [PMID: 37464213 PMCID: PMC10501969 DOI: 10.1007/s10278-023-00874-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/20/2023] [Accepted: 06/21/2023] [Indexed: 07/20/2023] Open
Abstract
Accurate segmentation of the liver and liver tumour (LT) is challenging due to its hazy boundaries and large shape variability. Although using U-Net for liver and LT segmentation achieves better results than manual segmentation, it loses spatial and channel features during segmentation, leading to inaccurate liver and LT segmentation. A residual deformable split depth-wise separable U-Net (RDSDSU-Net) is proposed to increase the accuracy of liver and LT segmentation. The residual deformable convolution layer (DCL) with deformable pooling (DP) is used in the encoder as an attention mechanism to adaptively extract liver and LT shape and position characteristics. Afterward, a convolutional spatial and channel features split graph network (CSCFSG-Net) is introduced in the middle processing layer to improve the expression capability of the liver and LT features by capturing spatial and channel features separately and to extract global contextual liver and LT information from spatial and channel features. Sub-pixel convolutions (SPC) are used in the decoder section to prevent the segmentation results from having a chequerboard artefact effect. Also, the residual deformable encoder features are combined with the decoder through summation to avoid increasing the number of feature maps (FM). Finally, the efficiency of the RDSDSU-Net is evaluated on the 3DIRCADb and LiTS datasets. The DICE score of the proposed RDSDSU-Net achieved 98.21% for liver segmentation and 93.25% for LT segmentation on 3DIRCADb. The experimental outcomes illustrate that the proposed RDSDSU-Net model achieved better segmentation results than the existing techniques.
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Affiliation(s)
- S Saumiya
- Department of ECE, Bethlahem Institute of Engineering, Karungal, Tamil Nadu India
| | - S Wilfred Franklin
- Department of ECE, CSI Institute of Technology, Thovalai, Tamil Nadu India
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Lakshmipriya B, Pottakkat B, Ramkumar G. Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review. Artif Intell Med 2023; 141:102557. [PMID: 37295904 DOI: 10.1016/j.artmed.2023.102557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 06/12/2023]
Abstract
Deep learning has become a thriving force in the computer aided diagnosis of liver cancer, as it solves extremely complicated challenges with high accuracy over time and facilitates medical experts in their diagnostic and treatment procedures. This paper presents a comprehensive systematic review on deep learning techniques applied for various applications pertaining to liver images, challenges faced by the clinicians in liver tumour diagnosis and how deep learning bridges the gap between clinical practice and technological solutions with an in-depth summary of 113 articles. Since, deep learning is an emerging revolutionary technology, recent state-of-the-art research implemented on liver images are reviewed with more focus on classification, segmentation and clinical applications in the management of liver diseases. Additionally, similar review articles in literature are reviewed and compared. The review is concluded by presenting the contemporary trends and unaddressed research issues in the field of liver tumour diagnosis, offering directions for future research in this field.
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Affiliation(s)
- B Lakshmipriya
- Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Biju Pottakkat
- Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.
| | - G Ramkumar
- Department of Radio Diagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
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Fogarollo S, Bale R, Harders M. Towards liver segmentation in the wild via contrastive distillation. Int J Comput Assist Radiol Surg 2023:10.1007/s11548-023-02912-3. [PMID: 37145251 PMCID: PMC10329587 DOI: 10.1007/s11548-023-02912-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/06/2023] [Indexed: 05/06/2023]
Abstract
PURPOSE Automatic liver segmentation is a key component for performing computer-assisted hepatic procedures. The task is challenging due to the high variability in organ appearance, numerous imaging modalities, and limited availability of labels. Moreover, strong generalization performance is required in real-world scenarios. However, existing supervised methods cannot be applied to data not seen during training (i.e. in the wild) because they generalize poorly. METHODS We propose to distill knowledge from a powerful model with our novel contrastive distillation scheme. We use a pre-trained large neural network to train our smaller model. A key novelty is to map neighboring slices close together in the latent representation, while mapping distant slices far away. Then, we use ground-truth labels to learn a U-Net style upsampling path and recover the segmentation map. RESULTS The pipeline is proven to be robust enough to perform state-of-the-art inference on target unseen domains. We carried out an extensive experimental validation using six common abdominal datasets, covering multiple modalities, as well as 18 patient datasets from the Innsbruck University Hospital. A sub-second inference time and a data-efficient training pipeline make it possible to scale our method to real-world conditions. CONCLUSION We propose a novel contrastive distillation scheme for automatic liver segmentation. A limited set of assumptions and superior performance to state-of-the-art techniques make our method a candidate for application to real-world scenarios.
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Affiliation(s)
- Stefano Fogarollo
- Department of Computer Science Interactive Graphics and Simulation Group (IGS), University of Innsbruck, Innsbruck, Austria.
| | - Reto Bale
- Interventional Oncology-Microinvasive Therapy (SIP), Department of Radiology, Medical University Innsbruck, Innsbruck, Austria
| | - Matthias Harders
- Department of Computer Science Interactive Graphics and Simulation Group (IGS), University of Innsbruck, Innsbruck, Austria
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Haseljić H, Chatterjee S, Frysch R, Kulvait V, Semshchikov V, Hensen B, Wacker F, Brüsch I, Werncke T, Speck O, Nürnberger A, Rose G. Liver segmentation using Turbolift learning for CT and cone-beam C-arm perfusion imaging. Comput Biol Med 2023; 154:106539. [PMID: 36689856 DOI: 10.1016/j.compbiomed.2023.106539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/30/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023]
Abstract
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-based CBCT data need to be segmented for proper visualisation and interpretation of perfusion maps. This research proposes Turbolift learning, which trains a modified version of the multi-scale Attention UNet on different liver segmentation tasks serially, following the order of the trainings CT, CBCT, CBCT TST - making the previous trainings act as pre-training stages for the subsequent ones - addressing the problem of limited number of datasets for training. For the final task of liver segmentation from CBCT TST, the proposed method achieved an overall Dice scores of 0.874±0.031 and 0.905±0.007 in 6-fold and 4-fold cross-validation experiments, respectively - securing statistically significant improvements over the model, which was trained only for that task. Experiments revealed that Turbolift not only improves the overall performance of the model but also makes it robust against artefacts originating from the embolisation materials and truncation artefacts. Additionally, in-depth analyses confirmed the order of the segmentation tasks. This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.
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Affiliation(s)
- Hana Haseljić
- Institute for Medical Engineering, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany
| | - Soumick Chatterjee
- Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany; Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Germany; Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Genomics Research Centre, Human Technopole, Milan, Italy.
| | - Robert Frysch
- Institute for Medical Engineering, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany
| | - Vojtěch Kulvait
- Institute of Materials Physics, Helmholtz-Zentrum hereon, Geesthacht, Germany
| | - Vladimir Semshchikov
- Institute for Medical Engineering, Otto von Guericke University Magdeburg, Germany
| | - Bennet Hensen
- Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Frank Wacker
- Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Inga Brüsch
- Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Institute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany
| | - Thomas Werncke
- Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Oliver Speck
- Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; German Center for Neurodegenerative Disease, Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Andreas Nürnberger
- Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany; Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Georg Rose
- Institute for Medical Engineering, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany
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Zuo H, Di W, Wang D, Shao C. The "Hand as Foot" teaching method in liver segment anatomy. Asian J Surg 2023; 46:1101-1102. [PMID: 35963675 DOI: 10.1016/j.asjsur.2022.07.153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 07/26/2022] [Indexed: 11/30/2022] Open
Affiliation(s)
- Huiying Zuo
- Department of School of Rehabilitation Medicine of Binzhou Medical University, Yantai, Shandong Province, 250012, China
| | - Weihua Di
- Department of Pain, Binzhou Medical University Hospital, Binzhou, Shandong Province, 256600, China
| | - Deqiang Wang
- Department of Pain, Binzhou Medical University Hospital, Binzhou, Shandong Province, 256600, China
| | - Cuijie Shao
- Department of Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong Province, 256603, China.
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Ansari MY, Yang Y, Meher PK, Dakua SP. Dense-PSP-UNet: A neural network for fast inference liver ultrasound segmentation. Comput Biol Med 2023; 153:106478. [PMID: 36603437 DOI: 10.1016/j.compbiomed.2022.106478] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/29/2022] [Accepted: 12/21/2022] [Indexed: 01/02/2023]
Abstract
Liver Ultrasound (US) or sonography is popularly used because of its real-time output, low-cost, ease-of-use, portability, and non-invasive nature. Segmentation of real-time liver US is essential for diagnosing and analyzing liver conditions (e.g., hepatocellular carcinoma (HCC)), assisting the surgeons/radiologists in therapeutic procedures. In this paper, we propose a method using a modified Pyramid Scene Parsing (PSP) module in tuned neural network backbones to achieve real-time segmentation without compromising the segmentation accuracy. Considering widespread noise in US data and its impact on outcomes, we study the impact of pre-processing and the influence of loss functions on segmentation performance. We have tested our method after annotating a publicly available US dataset containing 2400 images of 8 healthy volunteers (link to the annotated dataset is provided); the results show that the Dense-PSP-UNet model achieves a high Dice coefficient of 0.913±0.024 while delivering a real-time performance of 37 frames per second (FPS).
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Affiliation(s)
| | - Yin Yang
- Hamad Bin Khalifa Uinversity, Doha, Qatar
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Chen Y, Zheng C, Zhou T, Feng L, Liu L, Zeng Q, Wang G. A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans. Comput Biol Med 2023; 152:106421. [PMID: 36527780 DOI: 10.1016/j.compbiomed.2022.106421] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/17/2022] [Accepted: 12/11/2022] [Indexed: 12/15/2022]
Abstract
Liver tumours are diseases with high morbidity and high deterioration probabilities, and accurate liver area segmentation from computed tomography (CT) scans is a prerequisite for quick tumour diagnosis. While 2D network segmentation methods can perform segmentation with lower device performance requirements, they often discard the rich 3D spatial information contained in CT scans, limiting their segmentation accuracy. Hence, a deep residual attention-based U-shaped network (DRAUNet) with a biplane joint method for liver segmentation is proposed in this paper, where the biplane joint method introduces coronal CT slices to assist the transverse slices with segmentation, incorporating more 3D spatial information into the segmentation results to improve the segmentation performance of the network. Additionally, a novel deep residual block (DR block) and dual-effect attention module (DAM) are introduced in DRAUNet, where the DR block has deeper layers and two shortcut paths. The DAM efficiently combines the correlations of feature channels and the spatial locations of feature maps. The DRAUNet with the biplane joint method is tested on three datasets, Liver Tumour Segmentation (LiTS), 3D Image Reconstruction for Comparison of Algorithms Database (3DIRCADb), and Segmentation of the Liver Competition 2007 (Sliver07), and it achieves 97.3%, 97.4%, and 96.9% Dice similarity coefficients (DSCs) for liver segmentation, respectively, outperforming most state-of-the-art networks; this strongly demonstrates the segmentation performance of DRAUNet and the ability of the biplane joint method to obtain 3D spatial information from 3D images.
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Affiliation(s)
- Ying Chen
- School of Software, Nanchang Hangkong University, Nanchang, 330063, PR China
| | - Cheng Zheng
- School of Software, Nanchang Hangkong University, Nanchang, 330063, PR China.
| | - Taohui Zhou
- School of Software, Nanchang Hangkong University, Nanchang, 330063, PR China
| | - Longfeng Feng
- School of Software, Nanchang Hangkong University, Nanchang, 330063, PR China
| | - Lan Liu
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, 330029, PR China.
| | - Qiao Zeng
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, 330029, PR China
| | - Guoqing Wang
- Zhejiang Suosi Technology Co. Ltd, Wenzhou, 325000, PR China.
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Liu H, Fu Y, Zhang S, Liu J, Wang Y, Wang G, Fang J. GCHA-Net: Global context and hybrid attention network for automatic liver segmentation. Comput Biol Med 2023; 152:106352. [PMID: 36481761 DOI: 10.1016/j.compbiomed.2022.106352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/15/2022] [Accepted: 11/23/2022] [Indexed: 11/27/2022]
Abstract
Liver segmentation is a critical step in liver cancer diagnosis and surgical planning. The U-Net's architecture is one of the most efficient deep networks for medical image segmentation. However, the continuous downsampling operators in U-Net causes the loss of spatial information. To solve these problems, we propose a global context and hybrid attention network, called GCHA-Net, to adaptive capture the structural and detailed features. To capture the global features, a global attention module (GAM) is designed to model the channel and positional dimensions of the interdependencies. To capture the local features, a feature aggregation module (FAM) is designed, where a local attention module (LAM) is proposed to capture the spatial information. LAM can make our model focus on the local liver regions and suppress irrelevant information. The experimental results on the dataset LiTS2017 show that the dice per case (DPC) value and dice global (DG) value of liver were 96.5% and 96.9%, respectively. Compared with the state-of-the-art models, our model has superior performance in liver segmentation. Meanwhile, we test the experiment results on the 3Dircadb dataset, and it shows our model can obtain the highest accuracy compared with the closely related models. From these results, it can been seen that the proposed model can effectively capture the global context information and build the correlation between different convolutional layers. The code is available at the website: https://github.com/HuaxiangLiu/GCAU-Net.
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Affiliation(s)
- Huaxiang Liu
- Institute of Intelligent Information Processing, Taizhou University, Taizhou, 318000, Zhejiang, China
| | - Youyao Fu
- Institute of Intelligent Information Processing, Taizhou University, Taizhou, 318000, Zhejiang, China
| | - Shiqing Zhang
- Institute of Intelligent Information Processing, Taizhou University, Taizhou, 318000, Zhejiang, China
| | - Jun Liu
- College of Mechanical Engineering, Quzhou University, Quzhou, 324000, Zhejiang, China
| | - Yong Wang
- School of Aeronautics and Astronautics, Sun Yat Sen University, Guangzhou, 510275, Guangdong, China
| | - Guoyu Wang
- Institute of Intelligent Information Processing, Taizhou University, Taizhou, 318000, Zhejiang, China
| | - Jiangxiong Fang
- Institute of Intelligent Information Processing, Taizhou University, Taizhou, 318000, Zhejiang, China; College of Mechanical Engineering, Quzhou University, Quzhou, 324000, Zhejiang, China.
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Wang J, Zhang X, Lv P, Wang H, Cheng Y. Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT. J Digit Imaging 2022; 35:1479-1493. [PMID: 35711074 PMCID: PMC9712863 DOI: 10.1007/s10278-022-00668-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 05/30/2022] [Accepted: 06/03/2022] [Indexed: 10/18/2022] Open
Abstract
This paper proposes a new network framework, which leverages EfficientNetB4, attention gate, and residual learning techniques to achieve automatic and accurate liver segmentation. First, we use EfficientNetB4 as the encoder to extract more feature information during the encoding stage. Then, an attention gate is introduced in the skip connection to eliminate irrelevant regions and highlight features of a specific segmentation task. Finally, to alleviate the problem of gradient vanishment, we replace the traditional convolution of the decoder with a residual block to improve the segmentation accuracy. We verified the proposed method on the LiTS17 and SLiver07 datasets and compared it with classical networks such as FCN, U-Net, attention U-Net, and attention Res-U-Net. In the Sliver07 evaluation, the proposed method achieved the best segmentation performance on all five standard metrics. Meanwhile, in the LiTS17 assessment, the best performance is obtained except for a slight inferior on RVD. The proposed method's qualitative and quantitative results demonstrated its applicability in liver segmentation and proved its good prospect in computer-assisted liver segmentation.
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Affiliation(s)
- Jinke Wang
- Department of Software Engineering, Harbin University of Science and Technology, No. 2006, Xueyuan Road, Shandong Province, Rongcheng City, 264300, China.
- School of Automation, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Xiangyang Zhang
- School of Automation, Harbin University of Science and Technology, Harbin, 150080, China
| | - Peiqing Lv
- School of Automation, Harbin University of Science and Technology, Harbin, 150080, China
| | - Haiying Wang
- School of Automation, Harbin University of Science and Technology, Harbin, 150080, China
| | - Yuanzhi Cheng
- School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China
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Abstract
BACKGROUND Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk. AIM In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation. METHODS Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. CONCLUSION ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.
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Affiliation(s)
- Jason Tran
- Department of Medicine, University of Ottawa, Ottawa, Canada
| | - Divya Sharma
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Neta Gotlieb
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Wei Xu
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
- Division of Gastroenterology, Department of Medicine, University of Toronto, 585 University Avenue, Toronto, ON, M5G 2N2, Canada.
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Demir U, Zhang Z, Wang B, Antalek M, Keles E, Jha D, Borhani A, Ladner D, Bagci U. Transformer based Generative Adversarial Network for Liver Segmentation. Proc Int Conf Image Anal Process 2022; 13374:340-7. [PMID: 36745150 DOI: 10.1007/978-3-031-13324-4_29] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have became the standard image segmentation tasks, more recently this has started to change towards Transformers based architectures because Transformers are taking advantage of capturing long range dependence modeling capability in signals, so called attention mechanism. In this study, we propose a new segmentation approach using a hybrid approach combining the Transformer(s) with the Generative Adversarial Network (GAN) approach. The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling. This mechanism provides better segmentation performance compared with traditional methods. Furthermore, we encode this generator into the GAN based architecture so that the discriminator network in the GAN can classify the credibility of the generated segmentation masks compared with the real masks coming from human (expert) annotations. This allows us to extract the high dimensional topology information in the mask for biomedical image segmentation and provide more reliable segmentation results. Our model achieved a high dice coefficient of 0.9433, recall of 0.9515, and precision of 0.9376 and outperformed other Transformer based approaches. The implementation details of the proposed architecture can be found at https://github.com/UgurDemir/tranformer_liver_segmentation.
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Farzaneh N, Stein EB, Soroushmehr R, Gryak J, Najarian K. A deep learning framework for automated detection and quantitative assessment of liver trauma. BMC Med Imaging 2022; 22:39. [PMID: 35260105 PMCID: PMC8905785 DOI: 10.1186/s12880-022-00759-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 02/17/2022] [Indexed: 11/19/2022] Open
Abstract
Background Both early detection and severity assessment of liver trauma are critical for optimal triage and management of trauma patients. Current trauma protocols utilize computed tomography (CT) assessment of injuries in a subjective and qualitative (v.s. quantitative) fashion, shortcomings which could both be addressed by automated computer-aided systems that are capable of generating real-time reproducible and quantitative information. This study outlines an end-to-end pipeline to calculate the percentage of the liver parenchyma disrupted by trauma, an important component of the American Association for the Surgery of Trauma (AAST) liver injury scale, the primary tool to assess liver trauma severity at CT. Methods This framework comprises deep convolutional neural networks that first generate initial masks of both liver parenchyma (including normal and affected liver) and regions affected by trauma using three dimensional contrast-enhanced CT scans. Next, during the post-processing step, human domain knowledge about the location and intensity distribution of liver trauma is integrated into the model to avoid false positive regions. After generating the liver parenchyma and trauma masks, the corresponding volumes are calculated. Liver parenchymal disruption is then computed as the volume of the liver parenchyma that is disrupted by trauma. Results The proposed model was trained and validated on an internal dataset from the University of Michigan Health System (UMHS) including 77 CT scans (34 with and 43 without liver parenchymal trauma). The Dice/recall/precision coefficients of the proposed segmentation models are 96.13/96.00/96.35% and 51.21/53.20/56.76%, respectively, in segmenting liver parenchyma and liver trauma regions. In volume-based severity analysis, the proposed model yields a linear regression relation of 0.95 in estimating the percentage of liver parenchyma disrupted by trauma. The model shows an accurate performance in avoiding false positives for patients without any liver parenchymal trauma. These results indicate that the model is generalizable on patients with pre-existing liver conditions, including fatty livers and congestive hepatopathy. Conclusion The proposed algorithms are able to accurately segment the liver and the regions affected by trauma. This pipeline demonstrates an accurate performance in estimating the percentage of liver parenchyma that is affected by trauma. Such a system can aid critical care medical personnel by providing a reproducible quantitative assessment of liver trauma as an alternative to the sometimes subjective AAST grading system that is used currently. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00759-9.
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Affiliation(s)
- Negar Farzaneh
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA. .,The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, 48109, USA. .,Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Erica B Stein
- Department of Radiology, Michigan Medicine, Ann Arbor, MI, 48109, USA
| | - Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
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15
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Mohagheghi S, Foruzan AH. Developing an explainable deep learning boundary correction method by incorporating cascaded x-Dim models to improve segmentation defects in liver CT images. Comput Biol Med 2022; 140:105106. [PMID: 34864581 DOI: 10.1016/j.compbiomed.2021.105106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 11/16/2021] [Accepted: 11/29/2021] [Indexed: 11/21/2022]
Abstract
Deep learning methods achieved remarkable results in medical image analysis tasks but it has not yet been widely used by medical professionals. One of the main reasons for this restricted usage is the uncertainty of the reasons that influence the decision of the model. Explainable AI methods have been developed to improve the transparency, interpretability, and explainability of the black-box AI methods. The result of an explainable segmentation method will be more trusted by experts. In this study, we designed an explainable deep correction method by incorporating cascaded 1D and 2D models to refine the output of other models and provide reliable yet accurate results. We implemented a 2-step loop with a 1D local boundary validation model in the first step, and a 2D image patch segmentation model in the second step, to refine incorrect segmented regions slice-by-slice. The proposed method improved the result of the CNN segmentation models and achieved state-of-the-art results on 3D liver segmentation with the average dice coefficient of 98.27 on the Sliver07 dataset.
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Affiliation(s)
- Saeed Mohagheghi
- Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran.
| | - Amir Hossein Foruzan
- Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran.
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16
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Araújo JDL, da Cruz LB, Diniz JOB, Ferreira JL, Silva AC, de Paiva AC, Gattass M. Liver segmentation from computed tomography images using cascade deep learning. Comput Biol Med 2022; 140:105095. [PMID: 34902610 DOI: 10.1016/j.compbiomed.2021.105095] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/17/2021] [Accepted: 11/27/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Liver segmentation is a fundamental step in the treatment planning and diagnosis of liver cancer. However, manual segmentation of liver is time-consuming because of the large slice quantity and subjectiveness associated with the specialist's experience, which can lead to segmentation errors. Thus, the segmentation process can be automated using computational methods for better time efficiency and accuracy. However, automatic liver segmentation is a challenging task, as the liver can vary in shape, ill-defined borders, and lesions, which affect its appearance. We aim to propose an automatic method for liver segmentation using computed tomography (CT) images. METHODS The proposed method, based on deep convolutional neural network models and image processing techniques, comprise of four main steps: (1) image preprocessing, (2) initial segmentation, (3) reconstruction, and (4) final segmentation. RESULTS We evaluated the proposed method using 131 CT images from the LiTS image base. An average sensitivity of 95.45%, an average specificity of 99.86%, an average Dice coefficient of 95.64%, an average volumetric overlap error (VOE) of 8.28%, an average relative volume difference (RVD) of -0.41%, and an average Hausdorff distance (HD) of 26.60 mm were achieved. CONCLUSIONS This study demonstrates that liver segmentation, even when lesions are present in CT images, can be efficiently performed using a cascade approach and including a reconstruction step based on deep convolutional neural networks.
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Affiliation(s)
- José Denes Lima Araújo
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil.
| | - Luana Batista da Cruz
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil.
| | - João Otávio Bandeira Diniz
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil; Federal Institute of Maranhão, BR-226, SN, Campus Grajaú, Vila Nova, 65 940-000, Grajaú, MA, Brazil.
| | - Jonnison Lima Ferreira
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil; Federal Institute of Amazonas, Rua Santos Dumont, SN, Campus Tabatinga, Vila Verde, 69 640-000, Tabatinga, AM, Brazil.
| | - Aristófanes Corrêa Silva
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil.
| | - Anselmo Cardoso de Paiva
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil.
| | - Marcelo Gattass
- Pontifical Catholic University of Rio de Janeiro, R. São Vicente, 225, Gávea, 22 453-900, Rio de Janeiro, RJ, Brazil.
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17
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He B, Yin D, Chen X, Luo H, Xiao D, He M, Wang G, Fang C, Liu L, Jia F. A study of generalization and compatibility performance of 3D U-Net segmentation on multiple heterogeneous liver CT datasets. BMC Med Imaging 2021; 21:178. [PMID: 34819022 PMCID: PMC8611902 DOI: 10.1186/s12880-021-00708-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 11/15/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Most existing algorithms have been focused on the segmentation from several public Liver CT datasets scanned regularly (no pneumoperitoneum and horizontal supine position). This study primarily segmented datasets with unconventional liver shapes and intensities deduced by contrast phases, irregular scanning conditions, different scanning objects of pigs and patients with large pathological tumors, which formed the multiple heterogeneity of datasets used in this study. METHODS The multiple heterogeneous datasets used in this paper includes: (1) One public contrast-enhanced CT dataset and one public non-contrast CT dataset; (2) A contrast-enhanced dataset that has abnormal liver shape with very long left liver lobes and large-sized liver tumors with abnormal presets deduced by microvascular invasion; (3) One artificial pneumoperitoneum dataset under the pneumoperitoneum and three scanning profiles (horizontal/left/right recumbent position); (4) Two porcine datasets of Bama type and domestic type that contains pneumoperitoneum cases but with large anatomy discrepancy with humans. The study aimed to investigate the segmentation performances of 3D U-Net in: (1) generalization ability between multiple heterogeneous datasets by cross-testing experiments; (2) the compatibility when hybrid training all datasets in different sampling and encoder layer sharing schema. We further investigated the compatibility of encoder level by setting separate level for each dataset (i.e., dataset-wise convolutions) while sharing the decoder. RESULTS Model trained on different datasets has different segmentation performance. The prediction accuracy between LiTS dataset and Zhujiang dataset was about 0.955 and 0.958 which shows their good generalization ability due to that they were all contrast-enhanced clinical patient datasets scanned regularly. For the datasets scanned under pneumoperitoneum, their corresponding datasets scanned without pneumoperitoneum showed good generalization ability. Dataset-wise convolution module in high-level can improve the dataset unbalance problem. The experimental results will facilitate researchers making solutions when segmenting those special datasets. CONCLUSIONS (1) Regularly scanned datasets is well generalized to irregularly ones. (2) The hybrid training is beneficial but the dataset imbalance problem always exits due to the multi-domain homogeneity. The higher levels encoded more domain specific information than lower levels and thus were less compatible in terms of our datasets.
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Affiliation(s)
- Baochun He
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| | - Dalong Yin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, Harbin Medical University, Harbin, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, University of Science and Technology of China, Hefei, China
| | - Xiaoxia Chen
- Department of Radiology, The Third Medical Center, General Hospital of PLA, Beijing, China
| | - Huoling Luo
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| | - Deqiang Xiao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| | - Mu He
- First Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Guisheng Wang
- Department of Radiology, The Third Medical Center, General Hospital of PLA, Beijing, China
| | - Chihua Fang
- First Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Lianxin Liu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, Harbin Medical University, Harbin, China.
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, University of Science and Technology of China, Hefei, China.
| | - Fucang Jia
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China.
- Pazhou Lab, Guangzhou, China.
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Liu J, Xue D, Wang P. The "Hand as Foot" teaching method in liver segment anatomy. Asian J Surg 2021; 45:565-567. [PMID: 34801375 DOI: 10.1016/j.asjsur.2021.10.047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/12/2021] [Accepted: 10/10/2021] [Indexed: 11/26/2022] Open
Affiliation(s)
- Jianyu Liu
- Department of, Binzhou Medical University, Yantai, 250012, People's Republic of China; Department of Hepatobiliary Surgery, The People's Hospital of Binzhou, Binzhou, 256610, People's Republic of China
| | - Dong Xue
- Department of Hepatobiliary Surgery, The People's Hospital of Binzhou, Binzhou, 256610, People's Republic of China.
| | - Pingan Wang
- Department of Hepatobiliary Surgery, The People's Hospital of Binzhou, Binzhou, 256610, People's Republic of China
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Wu J, Zhou S, Zuo S, Chen Y, Sun W, Luo J, Duan J, Wang H, Wang D. U-Net combined with multi-scale attention mechanism for liver segmentation in CT images. BMC Med Inform Decis Mak 2021; 21:283. [PMID: 34654419 PMCID: PMC8520298 DOI: 10.1186/s12911-021-01649-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 10/04/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).
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Affiliation(s)
- Jiawei Wu
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Shengqiang Zhou
- School of Economics and Finance, Xi’an Jiaotong University, Xi’an, China
- Jiangsu Union Technical Institute, Xuzhou, China
| | - Songlin Zuo
- School of the First Clinical Medical, Xuzhou Medical University, Xuzhou, China
| | - Yiyin Chen
- School of the First Clinical Medical, Xuzhou Medical University, Xuzhou, China
| | - Weiqin Sun
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Jiang Luo
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Jiantuan Duan
- School of Economics and Finance, Xi’an Jiaotong University, Xi’an, China
| | - Hui Wang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Deguang Wang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Wang J, Lv P, Wang H, Shi C. SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography. Comput Methods Programs Biomed 2021; 208:106268. [PMID: 34274611 DOI: 10.1016/j.cmpb.2021.106268] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/29/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Liver segmentation is an essential prerequisite for liver cancer diagnosis and surgical planning. Traditionally, liver contour is delineated manually by radiologist in a slice-by-slice fashion. However, this process is time-consuming and prone to errors depending on radiologist's experience. In this paper, a modified U-Net based framework is presented, which leverages techniques from Squeeze-and-Excitation (SE) block, Atrous Spatial Pyramid Pooling (ASPP) and residual learning for accurate and robust liver Computed Tomography (CT) segmentation, and the effectiveness of the proposed method was tested on two public datasets LiTS17 and SLiver07. METHODS A new network architecture, called SAR-U-Net was designed, which is grounded in the classical U-Net. Firstly, the SE block is introduced to adaptively extract image features after each convolution in the U-Net encoder, while suppressing irrelevant regions, and highlighting features of specific segmentation task; Secondly, the ASPP is employed to replace the transition layer and the output layer, and acquire multi-scale image information via different receptive fields. Thirdly, to alleviate the gradient vanishment problem, the traditional convolution block is replaced with the residual structures, and thus prompt the network to gain accuracy from considerably increased depth. RESULTS In the LiTS17 database experiment, five popular metrics were used for evaluation, including Dice coefficient, VOE, RVD, ASD and MSD. Compared with other closely related models, the proposed method achieved the highest accuracy. In addition, in the experiment of the SLiver07 dataset, compared with other closely related models, the proposed method achieved the highest segmentation accuracy except for the RVD. CONCLUSION An improved U-Net network combining SE, ASPP, and residual structures is developed for automatic liver segmentation from CT images. This new model shows a great improvement on the accuracy compared to other closely related models, and its robustness to challenging problems, including small liver regions, discontinuous liver regions, and fuzzy liver boundaries, is also well demonstrated and validated.
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Affiliation(s)
- Jinke Wang
- Rongcheng College, Harbin University of Science and Technology, Rongcheng 264300, China; School of Automation, Harbin University of Science and Technology, Harbin 150080, China
| | - Peiqing Lv
- School of Automation, Harbin University of Science and Technology, Harbin 150080, China
| | - Haiying Wang
- School of Automation, Harbin University of Science and Technology, Harbin 150080, China
| | - Changfa Shi
- Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Technology and Business, Changsha 410205, China.
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22
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Chung M, Lee J, Park S, Lee CE, Lee J, Shin YG. Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention. Artif Intell Med 2021; 113:102023. [PMID: 33685586 DOI: 10.1016/j.artmed.2021.102023] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 11/13/2020] [Accepted: 01/18/2021] [Indexed: 12/25/2022]
Abstract
OBJECTIVE Accurate image segmentation of the liver is a challenging problem owing to its large shape variability and unclear boundaries. Although the applications of fully convolutional neural networks (CNNs) have shown groundbreaking results, limited studies have focused on the performance of generalization. In this study, we introduce a CNN for liver segmentation on abdominal computed tomography (CT) images that focus on the performance of generalization and accuracy. METHODS To improve the generalization performance, we initially propose an auto-context algorithm in a single CNN. The proposed auto-context neural network exploits an effective high-level residual estimation to obtain the shape prior. Identical dual paths are effectively trained to represent mutual complementary features for an accurate posterior analysis of a liver. Further, we extend our network by employing a self-supervised contour scheme. We trained sparse contour features by penalizing the ground-truth contour to focus more contour attentions on the failures. RESULTS We used 180 abdominal CT images for training and validation. Two-fold cross-validation is presented for a comparison with the state-of-the-art neural networks. The experimental results show that the proposed network results in better accuracy when compared to the state-of-the-art networks by reducing 10.31% of the Hausdorff distance. Novel multiple N-fold cross-validations are conducted to show the best performance of generalization of the proposed network. CONCLUSION AND SIGNIFICANCE The proposed method minimized the error between training and test images more than any other modern neural networks. Moreover, the contour scheme was successfully employed in the network by introducing a self-supervising metric.
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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. Comput Methods Programs Biomed 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Chung M, Lee J, Lee M, Lee J, Shin YG. Deeply self-supervised contour embedded neural network applied to liver segmentation. Comput Methods Programs Biomed 2020; 192:105447. [PMID: 32203792 DOI: 10.1016/j.cmpb.2020.105447] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 10/17/2019] [Accepted: 03/13/2020] [Indexed: 05/26/2023]
Abstract
OBJECTIVE Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography (CT) images. METHODS A fully convolutional network was developed to overcome the volumetric image segmentation problem. To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted as a complement with the global shape. The discriminative contour, shape, and deep features were internally merged for the segmentation results. RESULTS AND CONCLUSION 160 abdominal CT images were used for training and validation. The quantitative evaluation of the proposed network was performed through an eight-fold cross-validation. The result showed that the method, which uses the contour feature, segmented the liver more accurately than the state-of-the-art with a 2.13% improvement in the dice score. SIGNIFICANCE In this study, a new framework was introduced to guide a neural network and learn complementary contour features. The proposed neural network demonstrates that the guided contour features can significantly improve the performance of the segmentation task.
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Affiliation(s)
- Minyoung Chung
- School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea.
| | - Jingyu Lee
- School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea.
| | - Minkyung Lee
- School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea.
| | - Jeongjin Lee
- School of Computer Science and Engineering, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 156-743, Korea.
| | - Yeong-Gil Shin
- School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea.
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Zhang Q, Ren J, Qiao J. "Hand for liver" - A new approach for learning liver segmentation for medical students. Asian J Surg 2020; 43:1014-1015. [PMID: 32650960 DOI: 10.1016/j.asjsur.2020.05.033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 05/31/2020] [Indexed: 11/25/2022] Open
Affiliation(s)
- Qian Zhang
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Inner Mongolia Medical University, No.1 Tongdao North Street, Hohhot, 010050, PR China.
| | - Jianjun Ren
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Inner Mongolia Medical University, No.1 Tongdao North Street, Hohhot, 010050, PR China.
| | - Jianliang Qiao
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Inner Mongolia Medical University, No.1 Tongdao North Street, Hohhot, 010050, PR China.
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Abstract
Background Malignant liver tumor is one of the main causes of human death. In order to help physician better diagnose and make personalized treatment schemes, in clinical practice, it is often necessary to segment and visualize the liver tumor from abdominal computed tomography images. Due to the large number of slices in computed tomography sequence, developing an automatic and reliable segmentation method is very favored by physicians. However, because of the noise existed in the scan sequence and the similar pixel intensity of liver tumors with their surrounding tissues, besides, the size, position and shape of tumors also vary from one patient to another, automatic liver tumor segmentation is still a difficult task. Results We perform the proposed algorithm to the Liver Tumor Segmentation Challenge dataset and evaluate the segmentation results. Experimental results reveal that the proposed method achieved an average Dice score of 68.4% for tumor segmentation by using the designed network, and ASD, MSD, VOE and RVD improved from 27.8 to 21, 147 to 124, 0.52 to 0.46 and 0.69 to 0.73, respectively after performing adversarial training strategy, which proved the effectiveness of the proposed method. Conclusions The testing results show that the proposed method achieves improved performance, which corroborated the adversarial training based strategy can achieve more accurate and robustness results on liver tumor segmentation task.
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Affiliation(s)
- Lei Chen
- School of Computer Science & Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 100081, China
| | - Hong Song
- School of Computer Science & Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 100081, China.
| | - Chi Wang
- School of Computer Science & Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 100081, China
| | - Yutao Cui
- School of Computer Science & Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 100081, China
| | - Jian Yang
- School of Optics and Electronics & Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 100081, China
| | - Xiaohua Hu
- College of Computing & Informatics, Drexel University, Philadelphia, PA, USA
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Jafargholi Rangraz E, Coudyzer W, Maleux G, Baete K, Deroose CM, Nuyts J. Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization. EJNMMI Res 2019; 9:19. [PMID: 30788640 PMCID: PMC6382918 DOI: 10.1186/s13550-019-0485-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 01/29/2019] [Indexed: 12/15/2022] Open
Abstract
Purpose We have developed a multi-modal imaging approach for SIRT, combining 99mTc-MAA SPECT/CT and/or 90Y PET, 18F-FDG PET/CT, and contrast-enhanced CBCT for voxel-based dosimetry, as a tool for treatment planning and verification. For radiation dose prediction calculations, a segmentation of the total liver volume and of the liver perfusion territories is required. Method In this paper, we proposed a procedure for multi-modal image analysis to assist SIRT treatment planning. The pre-treatment 18F-FDG PET/CT, 99mTc-MAA SPECT/CT, and contrast-enhanced CBCT images were registered to a common space using an initial rigid, followed by a deformable registration. The registration was scored by an expert using Likert scores. The total liver was segmented semi-automatically based on the PET/CT and SPECT/CT images, and the liver perfusion territories were determined based on the CBCT images. The segmentations of the liver and liver lobes were compared to the manual segmentations by an expert on a CT image. Result Our methodology showed that multi-modal image analysis can be used for determination of the liver and perfusion territories using CBCT in SIRT using all pre-treatment studies. The results for image registration showed acceptable alignment with limited impact on dosimetry. The image registration performs well according to the expert reviewer (scored as perfect or with little misalignment in 94% of the cases). The semi-automatic liver segmentation agreed well with manual liver segmentation (dice coefficient of 0.92 and an average Hausdorff distance of 3.04 mm). The results showed disagreement between lobe segmentation using CBCT images compared to lobe segmentation based on CT images (average Hausdorff distance of 14.18 mm), with a high impact on the dosimetry (differences up to 9 Gy for right and 21 Gy for the left liver lobe). Conclusion This methodology can be used for pre-treatment dosimetry and for SIRT planning including the determination of the activity that should be administered to achieve the therapeutical goal. The inclusion of perfusion CBCT enables perfusion-based definition of the liver lobes, which was shown to be markedly different from the anatomical definition in some of the patients.
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Affiliation(s)
| | - Walter Coudyzer
- Radiology Section, Department of imaging and pathology, UZ Leuven, Leuven, Belgium
| | - Geert Maleux
- Radiology Section, Department of imaging and pathology, UZ & KU Leuven, Leuven, Belgium
| | - Kristof Baete
- Nuclear Medicine, Department of imaging and pathology, UZ & KU Leuven, Leuven, Belgium
| | - Christophe M Deroose
- Nuclear Medicine, Department of imaging and pathology, UZ & KU Leuven, Leuven, Belgium
| | - Johan Nuyts
- Nuclear Medicine, Department of imaging and pathology, UZ & KU Leuven, Leuven, Belgium
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Abstract
Apparent diffusion coefficient (ADC), derived from diffusion-weighted magnetic resonance images (DW-MRI), measures the motion of water molecules in vivo and can be used to quantify tumor response so as to determine the best therapy approach. In this paper, our goal was to determine whether the DW-MRI can be used for qualitative and quantitative liver cancer analysis, where an automated method will be proposed for improving the accuracy of liver segmentation in DW-MRI to increase the ability of diagnosis of disease. We firstly analyzed the research status of liver cancer diagnosis, especially on the issues of liver image segmentation technology in MRI. Then, the imaging mechanism and image features of the DW-MRI were analyzed, and the initial DW-MRI slice was segmented by graph-cut algorithm. Finally, our obtained result from the liver DW-MRI image is quantitatively and qualitatively analyzed. Experimental results show that DW-MRI has a great advantage in the diagnosis, the DWI images of benign lesion group was lower than that of malignant lesion, thus DW-MRI is segmented by graph-cut algorithm can provide important additional information regarding differential diagnosis of specific liver cancer to some extend.
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Affiliation(s)
| | - Yue Yang
- Tongde hospital of Zhejiang province, Zhejiang, 310012, Hangzhou, China.
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Spinczyk D, Krasoń A. Automatic liver segmentation in computed tomography using general-purpose shape modeling methods. Biomed Eng Online 2018; 17:65. [PMID: 29843736 PMCID: PMC5975396 DOI: 10.1186/s12938-018-0504-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 05/23/2018] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Liver segmentation in computed tomography is required in many clinical applications. The segmentation methods used can be classified according to a number of criteria. One important criterion for method selection is the shape representation of the segmented organ. The aim of the work is automatic liver segmentation using general purpose shape modeling methods. METHODS As part of the research, methods based on shape information at various levels of advancement were used. The single atlas based segmentation method was used as the simplest shape-based method. This method is derived from a single atlas using the deformable free-form deformation of the control point curves. Subsequently, the classic and modified Active Shape Model (ASM) was used, using medium body shape models. As the most advanced and main method generalized statistical shape models, Gaussian Process Morphable Models was used, which are based on multi-dimensional Gaussian distributions of the shape deformation field. RESULTS Mutual information and sum os square distance were used as similarity measures. The poorest results were obtained for the single atlas method. For the ASM method in 10 analyzed cases for seven test images, the Dice coefficient was above 55[Formula: see text], of which for three of them the coefficient was over 70[Formula: see text], which placed the method in second place. The best results were obtained for the method of generalized statistical distribution of the deformation field. The DICE coefficient for this method was 88.5[Formula: see text] CONCLUSIONS: This value of 88.5 [Formula: see text] Dice coefficient can be explained by the use of general-purpose shape modeling methods with a large variance of the shape of the modeled object-the liver and limitations on the size of our training data set, which was limited to 10 cases. The obtained results in presented fully automatic method are comparable with dedicated methods for liver segmentation. In addition, the deforamtion features of the model can be modeled mathematically by using various kernel functions, which allows to segment the liver on a comparable level using a smaller learning set.
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Affiliation(s)
- Dominik Spinczyk
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, Zabrze, Poland.
| | - Agata Krasoń
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, Zabrze, Poland
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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. Comput Methods Programs Biomed 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] [What about the content of this article? (0)] [Affiliation(s)] [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. Comput Methods Programs Biomed 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Siri SK, Latte MV. Combined endeavor of Neutrosophic Set and Chan-Vese model to extract accurate liver image from CT scan. Comput Methods Programs Biomed 2017; 151:101-109. [PMID: 28946992 DOI: 10.1016/j.cmpb.2017.08.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2017] [Revised: 06/29/2017] [Accepted: 08/22/2017] [Indexed: 06/07/2023]
Abstract
Many different diseases can occur in the liver, including infections such as hepatitis, cirrhosis, cancer and over effect of medication or toxins. The foremost stage for computer-aided diagnosis of liver is the identification of liver region. Liver segmentation algorithms extract liver image from scan images which helps in virtual surgery simulation, speedup the diagnosis, accurate investigation and surgery planning. The existing liver segmentation algorithms try to extort exact liver image from abdominal Computed Tomography (CT) scan images. It is an open problem because of ambiguous boundaries, large variation in intensity distribution, variability of liver geometry from patient to patient and presence of noise. A novel approach is proposed to meet challenges in extracting the exact liver image from abdominal CT scan images. The proposed approach consists of three phases: (1) Pre-processing (2) CT scan image transformation to Neutrosophic Set (NS) and (3) Post-processing. In pre-processing, the noise is removed by median filter. The "new structure" is designed to transform a CT scan image into neutrosophic domain which is expressed using three membership subset: True subset (T), False subset (F) and Indeterminacy subset (I). This transform approximately extracts the liver image structure. In post processing phase, morphological operation is performed on indeterminacy subset (I) and apply Chan-Vese (C-V) model with detection of initial contour within liver without user intervention. This resulted in liver boundary identification with high accuracy. Experiments show that, the proposed method is effective, robust and comparable with existing algorithm for liver segmentation of CT scan images.
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Affiliation(s)
- Sangeeta K Siri
- Department of Electronics & Communication Engineering, Sapthagiri College of Engineering, Bengaluru, karnataka 560057, India.
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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. Comput Methods Programs Biomed 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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Saito A, Yamamoto S, Nawano S, Shimizu A. Automated liver segmentation from a postmortem CT scan based on a statistical shape model. Int J Comput Assist Radiol Surg 2017; 12:205-21. [PMID: 27659283 DOI: 10.1007/s11548-016-1481-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 08/31/2016] [Indexed: 10/21/2022]
Abstract
PURPOSE Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a statistical shape model (SSM) for a postmortem liver. METHODS The location and shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation-maximization (EM) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. Algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label. RESULTS The average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with statistically significant difference. CONCLUSIONS We proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided EM algorithm estimated the location and shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.
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Lu F, Wu F, Hu P, Peng Z, Kong D. Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg. 2017;12:171-182. [PMID: 27604760 DOI: 10.1007/s11548-016-1467-3] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 07/26/2016] [Indexed: 12/12/2022]
Abstract
PURPOSE Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. METHODS The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. RESULTS The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. CONCLUSIONS The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.
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38
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Bereciartua A, Picon A, Galdran A, Iriondo P. 3D active surfaces for liver segmentation in multisequence MRI images. Comput Methods Programs Biomed 2016; 132:149-160. [PMID: 27282235 DOI: 10.1016/j.cmpb.2016.04.028] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 03/10/2016] [Accepted: 04/26/2016] [Indexed: 06/06/2023]
Abstract
Biopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59.
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Affiliation(s)
- Arantza Bereciartua
- Tecnalia Research & Innovation, Computer Vision Area, Parque Tecnológico de Bizkaia, Derio 48160, Spain.
| | - Artzai Picon
- Tecnalia Research & Innovation, Computer Vision Area, Parque Tecnológico de Bizkaia, Derio 48160, Spain
| | - Adrian Galdran
- Tecnalia Research & Innovation, Computer Vision Area, Parque Tecnológico de Bizkaia, Derio 48160, Spain
| | - Pedro Iriondo
- Department of System Engineering and Automatic, University of the Basque Country, Bilbao, Spain
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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40
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Lu D, Wu Y, Harris G, Cai W. Iterative mesh transformation for 3D segmentation of livers with cancers in CT images. Comput Med Imaging Graph 2015; 43:1-14. [PMID: 25728595 DOI: 10.1016/j.compmedimag.2015.01.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Revised: 11/20/2014] [Accepted: 01/09/2015] [Indexed: 01/26/2023]
Abstract
Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh transformation deforms the 3D mesh based on the deformation transfer model that searches the optimal mesh based on the affine transformation subjected to a set of constraints of targeting vertices. Besides, contour optimization searches the optimal transversal contours of the ROI by applying the dynamic-programming algorithm to the intersection polylines of the 3D mesh on 2D transversal image planes. The initial constraint set for mesh transformation can be defined by a very small number of targeting vertices, namely landmarks, and progressively updated by adding the targeting vertices selected from the optimal transversal contours calculated in contour optimization. This iterative 3D mesh transformation constrained by 2D optimal transversal contours provides an efficient solution to a progressive approximation of the mesh of the targeting ROI. Based on this iterative mesh transformation algorithm, we developed a semi-automated scheme for segmentation of diseased livers with cancers using as little as five user-identified landmarks. The evaluation study demonstrates that this semi-automated liver segmentation scheme can achieve accurate and reliable segmentation results with significant reduction of interaction time and efforts when dealing with diseased liver cases.
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Affiliation(s)
- Difei Lu
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA; Department of Informatics, Zhejiang Police College, China
| | - Yin Wu
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Gordon Harris
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Wenli Cai
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA.
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41
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Göçeri E, Gürcan MN, Dicle O. Fully automated liver segmentation from SPIR image series. Comput Biol Med 2014; 53:265-78. [PMID: 25192606 DOI: 10.1016/j.compbiomed.2014.08.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Revised: 08/04/2014] [Accepted: 08/10/2014] [Indexed: 10/24/2022]
Abstract
Accurate liver segmentation is an important component of surgery planning for liver transplantation, which enables patients with liver disease a chance to survive. Spectral pre-saturation inversion recovery (SPIR) image sequences are useful for liver vessel segmentation because vascular structures in the liver are clearly visible in these sequences. Although level-set based segmentation techniques are frequently used in liver segmentation due to their flexibility to adapt to different problems by incorporating prior knowledge, the need to initialize the contours on each slice is a common drawback of such techniques. In this paper, we present a fully automated variational level set approach for liver segmentation from SPIR image sequences. Our approach is designed to be efficient while achieving high accuracy. The efficiency is achieved by (1) automatically defining an initial contour for each slice, and (2) automatically computing weight values of each term in the applied energy functional at each iteration during evolution. Automated detection and exclusion of spurious structures (e.g. cysts and other bright white regions on the skin) in the pre-processing stage increases the accuracy and robustness. We also present a novel approach to reduce computational cost by employing binary regularization of level set function. A signed pressure force function controls the evolution of the active contour. The method was applied to ten data sets. In each image, the performance of the algorithm was measured using the receiver operating characteristics method in terms of accuracy, sensitivity and specificity. The accuracy of the proposed method was 96%. Quantitative analyses of results indicate that the proposed method can accurately, efficiently and consistently segment liver images.
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Affiliation(s)
- Evgin Göçeri
- Department of Computer Engineering, Pamukkale University, Denizli, Turkey.
| | - Metin N Gürcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Oğuz Dicle
- Department of Radiology, Faculty of Medicine, Dokuz Eylul University, Narlıdere, Izmir, Turkey
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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. Comput Methods Programs Biomed 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Tomoshige S, Oost E, Shimizu A, Watanabe H, Nawano S. A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast CT images. Med Image Anal 2013; 18:130-43. [PMID: 24184436 DOI: 10.1016/j.media.2013.10.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 10/03/2013] [Accepted: 10/07/2013] [Indexed: 10/26/2022]
Abstract
This paper presents a novel conditional statistical shape model in which the condition can be relaxed instead of being treated as a hard constraint. The major contribution of this paper is the integration of an error model that estimates the reliability of the observed conditional features and subsequently relaxes the conditional statistical shape model accordingly. A three-step pipeline consisting of (1) conditional feature extraction from a maximum a posteriori estimation, (2) shape prior estimation through the novel level set based conditional statistical shape model with integrated error model and (3) subsequent graph cuts segmentation based on the estimated shape prior is applied to automatic liver segmentation from non-contrast abdominal CT volumes. Comparison with three other state of the art methods shows the superior performance of the proposed algorithm.
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Affiliation(s)
- Sho Tomoshige
- Tokyo University of Agriculture and Technology, Nakacho 2-24-16, Koganei, Tokyo 184-8588, Japan
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Abstract
Since the concept of liver segmental anatomy was first put forward by Francis Glisson in 1654, Hjortsjo segmentation, Healey arteriobiliary segmentation, Couinaud portal and hepatic vein segmentation have been proposed. Although the nomenclature of hepatic anatomy and resections was introduced in the international conference of HPB held in Brisbane, Australia in 2000, the development of liver anatomical techniques (such as iconography and virtual digital technique) as well as the constantly updating knowledge about segmentation of the liver lobe and understanding of the anatomy have raised some new issues. Retrospective analysis and understanding of the features of various segmentation methods and the rules of clinical practice will help us find the most suitable idea of "precise liver resection" in nowadays.
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45
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Yang X, Yu HC, Choi Y, Lee W, Wang B, Yang J, Hwang H, Kim JH, Song J, Cho BH, You H. A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points. Comput Methods Programs Biomed 2013; 113:69-79. [PMID: 24113421 DOI: 10.1016/j.cmpb.2013.08.019] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Revised: 08/27/2013] [Accepted: 08/29/2013] [Indexed: 06/02/2023]
Abstract
The present study developed a hybrid semi-automatic method to extract the liver from abdominal computerized tomography (CT) images. The proposed hybrid method consists of a customized fast-marching level-set method for detection of an optimal initial liver region from multiple seed points selected by the user and a threshold-based level-set method for extraction of the actual liver region based on the initial liver region. The performance of the hybrid method was compared with those of the 2D region growing method implemented in OsiriX using abdominal CT datasets of 15 patients. The hybrid method showed a significantly higher accuracy in liver extraction (similarity index, SI=97.6 ± 0.5%; false positive error, FPE = 2.2 ± 0.7%; false negative error, FNE=2.5 ± 0.8%; average symmetric surface distance, ASD=1.4 ± 0.5mm) than the 2D (SI=94.0 ± 1.9%; FPE = 5.3 ± 1.1%; FNE=6.5 ± 3.7%; ASD=6.7 ± 3.8mm) region growing method. The total liver extraction time per CT dataset of the hybrid method (77 ± 10 s) is significantly less than the 2D region growing method (575 ± 136 s). The interaction time per CT dataset between the user and a computer of the hybrid method (28 ± 4 s) is significantly shorter than the 2D region growing method (484 ± 126 s). The proposed hybrid method was found preferred for liver segmentation in preoperative virtual liver surgery planning.
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Affiliation(s)
- Xiaopeng Yang
- Pohang University of Science and Technology, Pohang 790-784, South Korea
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