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Salehi MA, Harandi H, Mohammadi S, Shahrabi Farahani M, Shojaei S, Saleh RR. Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1297-1311. [PMID: 38438694 PMCID: PMC11300422 DOI: 10.1007/s10278-024-01058-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/18/2024] [Accepted: 02/19/2024] [Indexed: 03/06/2024]
Abstract
Due to the increasing interest in the use of artificial intelligence (AI) algorithms in hepatocellular carcinoma detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI and to compare them with clinicians' performance. A search in PubMed and Scopus was performed in January 2024 to find studies that evaluated and/or validated an AI algorithm for the detection of HCC. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the modality of imaging and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST) reporting guidelines. Out of 3177 studies screened, 44 eligible studies were included. The pooled sensitivity and specificity for internally validated AI algorithms were 84% (95% CI: 81,87) and 92% (95% CI: 90,94), respectively. Externally validated AI algorithms had a pooled sensitivity of 85% (95% CI: 78,89) and specificity of 84% (95% CI: 72,91). When clinicians were internally validated, their pooled sensitivity was 70% (95% CI: 60,78), while their pooled specificity was 85% (95% CI: 77,90). This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by screening images and highlighting regions of interest, thus improving workflow.
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Affiliation(s)
| | - Hamid Harandi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Soheil Mohammadi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | | | - Shayan Shojaei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Ramy R Saleh
- Department of Oncology, McGill University, Montreal, QC, H3A 0G4, Canada
- Division of Medical Oncology, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
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2
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Tang VH, Duong STM, Nguyen CDT, Huynh TM, Duc VT, Phan C, Le H, Bui T, Truong SQH. Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison. Sci Rep 2023; 13:19559. [PMID: 37950031 PMCID: PMC10638447 DOI: 10.1038/s41598-023-46695-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023] Open
Abstract
Early detection of liver malignancy based on medical image analysis plays a crucial role in patient prognosis and personalized treatment. This task, however, is challenging due to several factors, including medical data scarcity and limited training samples. This paper presents a study of three important aspects of radiomics feature from multiphase computed tomography (CT) for classifying hepatocellular carcinoma (HCC) and other focal liver lesions: wavelet-transformed feature extraction, relevant feature selection, and radiomics features-based classification under the inadequate training samples. Our analysis shows that combining radiomics features extracted from the wavelet and original CT domains enhance the classification performance significantly, compared with using those extracted from the wavelet or original domain only. To facilitate the multi-domain and multiphase radiomics feature combination, we introduce a logistic sparsity-based model for feature selection with Bayesian optimization and find that the proposed model yields more discriminative and relevant features than several existing methods, including filter-based, wrapper-based, or other model-based techniques. In addition, we present analysis and performance comparison with several recent deep convolutional neural network (CNN)-based feature models proposed for hepatic lesion diagnosis. The results show that under the inadequate data scenario, the proposed wavelet radiomics feature model produces comparable, if not higher, performance metrics than the CNN-based feature models in terms of area under the curve.
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Affiliation(s)
- Van Ha Tang
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- Le Quy Don Technical University, 236 Hoang Quoc Viet, Hanoi, 11917, Vietnam
| | - Soan T M Duong
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam.
- Le Quy Don Technical University, 236 Hoang Quoc Viet, Hanoi, 11917, Vietnam.
| | - Chanh D Tr Nguyen
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
| | - Thanh M Huynh
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
| | - Vo T Duc
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Chien Phan
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Huyen Le
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Trung Bui
- Adobe Research, San Francisco, CA, 94103, USA
| | - Steven Q H Truong
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
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3
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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] [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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Jin R, Wang M, Xu L, Lu J, Song E, Ma G. Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour. Med Phys 2022; 50:2100-2120. [PMID: 36413182 DOI: 10.1002/mp.16116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 10/27/2022] [Accepted: 11/05/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learning methods. METHODS The proposed method first constructs a slice-indexed-histogram to localize the volume of interest (VOI) and estimate the probability that a voxel belongs to the liver according its intensity. The probabilistic image would be used to initialize the 3D PAC model. Secondly, a new contour indicator function, which is a component of the model, is produced by combining the gradient-based edge detection and Hessian-matrix-based surface detection. Then, a fast numerical scheme derived for the 3D PAC model is performed to evolve the initial probabilistic image into the global minimizer of the model, which is a smoothed probabilistic image showing a distinctly highlighted liver. Next, a simple region-growing strategy is applied to extract the whole liver mask from the smoothed probabilistic image. Finally, a B-spline surface is constructed to fit the patch of the rib cage to prevent possible leakage into adjacent intercostal tissues. RESULTS The proposed method is evaluated on two public datasets. The average Dice score, volume overlap error, volume difference, symmetric surface distance and volume processing time are 0.96, 7.35%, 0.02%, 1.17 mm and 19.8 s for the Sliver07 dataset, and 0.95, 8.89%, - 0.02 % $-0.02\%$ , 1.45 mm and 23.08 s for the 3Dircadb dataset, respectively. CONCLUSIONS The proposed fully-automatic approach can effectively segment the liver from low-contrast and complex backgrounds. The quantitative and qualitative results demonstrate that the proposed segmentation method outperforms state-of-the-art traditional automatic liver segmentation algorithms and achieves very competitive performance compared with recent deep leaning-based methods.
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Affiliation(s)
- Renchao Jin
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Manyang Wang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Lijun Xu
- School of Computer and Information Engineering, Hubei University, Wuhan, China
| | - Jiayi Lu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guangzhi Ma
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
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5
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Martinino A, Aloulou M, Chatterjee S, Scarano Pereira JP, Singhal S, Patel T, Kirchgesner TPE, Agnes S, Annunziata S, Treglia G, Giovinazzo F. Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review. J Clin Med 2022; 11:6368. [PMID: 36362596 PMCID: PMC9655417 DOI: 10.3390/jcm11216368] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 09/21/2023] Open
Abstract
Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.
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Affiliation(s)
| | | | - Surobhi Chatterjee
- Department of Internal Medicine, King George’s Medical University, Lucknow 226003, Uttar Pradesh, India
| | | | - Saurabh Singhal
- Department of HPB Surgery and Liver Transplantation, BLK-MAX Superspeciality Hospital, New Delhi 110005, Delhi, India
| | - Tapan Patel
- Department of Surgery, Baroda Medical College and SSG Hospital, Vadodara 390001, Gujarat, India
| | - Thomas Paul-Emile Kirchgesner
- Département of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, 1348 Brussels, Belgium
| | - Salvatore Agnes
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Salvatore Annunziata
- Unit of Nuclear Medicine, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Giorgio Treglia
- Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Francesco Giovinazzo
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
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6
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Rahman H, Bukht TFN, Imran A, Tariq J, Tu S, Alzahrani A. A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet. Bioengineering (Basel) 2022; 9:bioengineering9080368. [PMID: 36004893 PMCID: PMC9404984 DOI: 10.3390/bioengineering9080368] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 11/25/2022] Open
Abstract
According to the most recent estimates from global cancer statistics for 2020, liver cancer is the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting the tumor from the liver adds some difficulty. After a sample of liver tissue is taken, imaging tests, such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US), are used to segment the liver and liver tumor. Due to overlapping intensity and variability in the position and shape of soft tissues, segmentation of the liver and tumor from computed abdominal tomography images based on shade gray or shapes is undesirable. This study proposed a more efficient method for segmenting liver and tumors from CT image volumes using a hybrid ResUNet model, combining the ResNet and UNet models to address this gap. The two overlapping models were primarily used in this study to segment the liver and for region of interest (ROI) assessment. Segmentation of the liver is done to examine the liver with an abdominal CT image volume. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01. Based on the experimental analysis, the true value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16%. The authentication rate of the dice coefficient also increased, indicating that the experiment went well and that the model is ready to use for the detection of liver tumors.
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Affiliation(s)
- Hameedur Rahman
- Department of Creative Technologies, Faculty of Computing & AI, Air University PAF Complex, Islamabad 44000, Pakistan
- Faculty of Information Technology, Beijing University of Technology, Beijing 100024, China
| | | | - Azhar Imran
- Department of Creative Technologies, Faculty of Computing & AI, Air University PAF Complex, Islamabad 44000, Pakistan
| | - Junaid Tariq
- Department of Computer Science, National University of Modern Languages (NUML), Rawalpindi Campus, Islamabad 44000, Pakistan
| | - Shanshan Tu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100024, China
- Correspondence:
| | - Abdulkareeem Alzahrani
- Computer Engineering and Science Department, Faculty of Computer Science and Information Technology, Al Baha University, Al Baha 65515, Saudi Arabia
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7
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Altini N, Prencipe B, Cascarano GD, Brunetti A, Brunetti G, Triggiani V, Carnimeo L, Marino F, Guerriero A, Villani L, Scardapane A, Bevilacqua V. Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.08.157] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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8
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Ansari MY, Abdalla A, Ansari MY, Ansari MI, Malluhi B, Mohanty S, Mishra S, Singh SS, Abinahed J, Al-Ansari A, Balakrishnan S, Dakua SP. Practical utility of liver segmentation methods in clinical surgeries and interventions. BMC Med Imaging 2022; 22:97. [PMID: 35610600 PMCID: PMC9128093 DOI: 10.1186/s12880-022-00825-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 05/09/2022] [Indexed: 12/15/2022] Open
Abstract
Clinical imaging (e.g., magnetic resonance imaging and computed tomography) is a crucial adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate interventions. This is especially true in malignant conditions such as hepatocellular carcinoma (HCC), where image segmentation (such as accurate delineation of liver and tumor) is the preliminary step taken by the clinicians to optimize diagnosis, staging, and treatment planning and intervention (e.g., transplantation, surgical resection, radiotherapy, PVE, embolization, etc). Thus, segmentation methods could potentially impact the diagnosis and treatment outcomes. This paper comprehensively reviews the literature (during the year 2012-2021) for relevant segmentation methods and proposes a broad categorization based on their clinical utility (i.e., surgical and radiological interventions) in HCC. The categorization is based on the parameters such as precision, accuracy, and automation.
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9
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Ye B, Yuan X, Cai Z, Lan T. Severity Assessment of COVID-19 Based on Feature Extraction and V-Descriptors. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021; 17:7456-7467. [PMID: 37982011 PMCID: PMC8545021 DOI: 10.1109/tii.2021.3056386] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 01/04/2021] [Accepted: 01/28/2021] [Indexed: 11/21/2023]
Abstract
Digital image feature recognition is significant to industrial information applications, such as bioengineering, medical diagnosis, and machinery industry. In order to supply an effective and reasonable technology of the severity assessment mission of coronavirus disease (COVID-19), in this article, we propose a new method that identifies rich features of lung infections from a chest computed tomography (CT) image, and then assesses the severity of COVID-19 based on the extracted features. First, in a chest CT image, the lung contours are corrected for the segmentation of bilateral lungs. Then, the lung contours and areas are obtained from the lung regions. Next, the coarseness, contrast, roughness, and entropy texture features are extracted to confirm the COVID-19 infected regions, and then the lesion contours are extracted from the infected regions. Finally, the texture features and V-descriptors are fused as an assessment descriptor for the COVID-19 severity estimation. In the experiments, we show the feature extraction and lung lesion segmentation results based on some typical COVID-19 infected CT images. In the lesion contour reconstruction experiments, the performance of V-descriptors is compared with some different methods, and various feature scores indicate that the proposed assessment descriptor reflects the infected ratio and the density feature of the lesions well, which can estimate the severity of COVID-19 infection more accurately.
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Affiliation(s)
- Ben Ye
- Faculty of Information TechnologyMacau University of Science and TechnologyMacau999078China
| | - Xixi Yuan
- Faculty of Information TechnologyMacau University of Science and TechnologyMacau999078China
| | - Zhanchuan Cai
- Faculty of Information TechnologyMacau University of Science and TechnologyMacau999078China
| | - Ting Lan
- Faculty of Information TechnologyMacau University of Science and TechnologyMacau999078China
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10
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Gong M, Chen S, Chen Q, Zeng Y, Zhang Y. Generative Adversarial Networks in Medical Image Processing. Curr Pharm Des 2021; 27:1856-1868. [PMID: 33238866 DOI: 10.2174/1381612826666201125110710] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/14/2020] [Accepted: 10/21/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND The emergence of generative adversarial networks (GANs) has provided new technology and framework for the application of medical images. Specifically, a GAN requires little to no labeled data to obtain high-quality data that can be generated through competition between the generator and discriminator networks. Therefore, GANs are rapidly proving to be a state-of-the-art foundation, achieving enhanced performances in various medical applications. METHODS In this article, we introduce the principles of GANs and their various variants, deep convolutional GAN, conditional GAN, Wasserstein GAN, Info-GAN, boundary equilibrium GAN, and cycle-GAN. RESULTS All various GANs have found success in medical imaging tasks, including medical image enhancement, segmentation, classification, reconstruction, and synthesis. Furthermore, we summarize the data processing methods and evaluation indicators. Finally, we note the limitations of existing methods and the existing challenges that need to be addressed in this field. CONCLUSION Although GANs are in the initial stage of development in medical image processing, it will have a great prospect in the future.
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Affiliation(s)
- Meiqin Gong
- West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Siyu Chen
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Qingyuan Chen
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yuanqi Zeng
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
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11
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Al-Masni MA, Kim DH. CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation. Sci Rep 2021; 11:10191. [PMID: 33986375 PMCID: PMC8119726 DOI: 10.1038/s41598-021-89686-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 04/26/2021] [Indexed: 01/20/2023] Open
Abstract
Medical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical "OR" and "AND" operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations.
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Affiliation(s)
- Mohammed A Al-Masni
- Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea.
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12
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Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy. Diagnostics (Basel) 2021; 11:diagnostics11050852. [PMID: 34068516 PMCID: PMC8151903 DOI: 10.3390/diagnostics11050852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/03/2021] [Accepted: 05/08/2021] [Indexed: 02/02/2023] Open
Abstract
Accurate localization and analyses of functional liver segments are crucial in devising various surgical procedures, including hepatectomy. To this end, they require the extraction of a liver from computed tomography, and then the identification of resection correspondence between individuals. The first part is usually impeded by inherent deficiencies, as present in medical images, and vast anatomical variations across subjects. While the model-based approach is found viable to tackle both issues, it is often undermined by an inadequate number of labeled samples, to capture all plausible variations. To address segmentation problems by balancing between accuracy, resource consumption, and data availability, this paper presents an efficient method for liver segmentation based on a graph-cut algorithm. One of its main novelties is the incorporation of a feature preserving a metric for boundary separation. Intuitive anatomical constraints are imposed to ensure valid extraction. The second part involves the symmetric conformal parameterization of the extracted liver surface onto a genus-0 domain. Provided with a few landmarks specified on two livers, we demonstrated that, by using a modified Beltrami differential, not only could they be non-rigidly registered, but also the hepatectomy on one liver could be envisioned on another. The merits of the proposed scheme were elucidated by both visual and numerical assessments on a standard MICCAI SLIVER07 dataset.
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13
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Semi-automatic liver segmentation based on probabilistic models and anatomical constraints. Sci Rep 2021; 11:6106. [PMID: 33731736 PMCID: PMC7969941 DOI: 10.1038/s41598-021-85436-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 03/01/2021] [Indexed: 12/12/2022] Open
Abstract
Segmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.
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14
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Wu PH, Bedoya M, White J, Brace CL. Feature-based automated segmentation of ablation zones by fuzzy c-mean clustering during low-dose computed tomography. Med Phys 2021; 48:703-714. [PMID: 33237594 PMCID: PMC8594246 DOI: 10.1002/mp.14623] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/21/2020] [Accepted: 08/10/2020] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Intra-procedural monitoring and post-procedural follow-up is necessary for a successful ablation treatment. An imaging technique which can assess the ablation geometry accurately is beneficial to monitor and evaluate treatment. In this study, we developed an automated ablation segmentation technique for serial low-dose, noisy ablation computed tomography (CT) or contrast-enhanced CT (CECT). METHODS Low-dose, noisy temporal CT and CECT volumes were acquired during microwave ablation on normal porcine liver (four with non-contrast CT and eight with CECT). Highly constrained backprojection (HYPR) processing was used to recover ablation zone information compromised by low-dose noise. First-order statistic features and normalized fractional Brownian features (NBF) were used to segment ablation zones by fuzzy c-mean clustering. After clustering, the segmented ablation zone was refined by cyclic morphological processing. Automatic and manual segmentations were compared to gross pathology with Dice's coefficient (morphological similarity), while cross-sectional dimensions were compared by percent difference. RESULTS Automatic and manual segmentations of the ablation zone were very similar to gross pathology (Dice Coefficients: Auto.-Path. = 0.84 ± 0.02; Manu.-Path. = 0.76 ± 0.03, P = 0.11). The differences in ablation area, major diameter and minor diameter were 17.9 ± 3.2%, 11.1 ± 3.2% and 16.2 ± 3.4%, respectively, when comparing automatic segmentation to gross pathology, which were lower than the differences of 32.9 ± 16.8%, 13.0 ± 9.8% and 21.8 ± 5.8% when comparing manual segmentation to gross pathology. Manual segmentations tended to overestimate gross pathology when ablation area was less than 15 cm2 , but the automated segmentation tended to underestimate gross pathology when ablation zone is larger than 20 cm2 . CONCLUSION Fuzzy c-means clustering may be used to aid automatic segmentation of ablation zones without prior information or user input, making serial CT/CECT has more potential to assess treatments intra-procedurally.
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Affiliation(s)
- Po-hung Wu
- Department of Electrical and Computer Engineering, University of Wisconsin - Madison, 1415 Engineering Drive, Madison, WI 53706, USA
| | - Mariajose Bedoya
- Department of Medical Physics, University of Wisconsin - Madison, 1111 Highland Ave, Rm 1005, Madison, WI 53705, USA
| | - Jim White
- Department of Biomedical Engineering, University of Wisconsin - Madison, 1415 Engineering Drive, Madison, WI 53706, USA
| | - Christopher L. Brace
- Department of Biomedical Engineering, University of Wisconsin - Madison, 1415 Engineering Drive, Madison, WI 53706, USA
- Department of Radiology, University of Wisconsin - Madison, 1111 Highland Ave, Madison, WI 53705, USA
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15
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Krishan A, Mittal D. Ensembled liver cancer detection and classification using CT images. Proc Inst Mech Eng H 2020; 235:232-244. [PMID: 33183141 DOI: 10.1177/0954411920971888] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Computed tomography (CT) images are commonly used to diagnose liver disease. It is sometimes very difficult to comment on the type, category and level of the tumor, even for experienced radiologists, directly from the CT image, due to the varying intensities. In recent years, it has been important to design and develop computer-assisted imaging techniques to help doctors/physicians improve their diagnosis. The proposed work is to detect the presence of a tumor region in the liver and classify the different stages of the tumor from CT images. CT images of the liver have been classified between normal and tumor classes. In addition, CT images of the tumor have been classified between Hepato Cellular Carcinoma (HCC) and Metastases (MET). The performance of six different classifiers was evaluated on different parameters. The accuracy achieved for different classifiers varies between 98.39% and 100% for tumor identification and between 76.38% and 87.01% for tumor classification. To further, improve performance, a multi-level ensemble model is developed to detect a tumor (liver cancer) and to classify between HCC and MET using features extracted from CT images. The k-fold cross-validation (CV) is also used to justify the robustness of the classifiers. Compared to the individual classifier, the multi-level ensemble model achieved high accuracy in both the detection and classification of different tumors. This study demonstrates automated tumor characterization based on liver CT images and will assist the radiologist in detecting and classifying different types of tumors at a very early stage.
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Affiliation(s)
- Abhay Krishan
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, India
| | - Deepti Mittal
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, India
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16
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Nayantara PV, Kamath S, Manjunath KN, Rajagopal KV. Computer-aided diagnosis of liver lesions using CT images: A systematic review. Comput Biol Med 2020; 127:104035. [PMID: 33099219 DOI: 10.1016/j.compbiomed.2020.104035] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 01/17/2023]
Abstract
BACKGROUND Medical image processing has a strong footprint in radio diagnosis for the detection of diseases from the images. Several computer-aided systems were researched in the recent past to assist the radiologist in diagnosing liver diseases and reducing the interpretation time. The aim of this paper is to provide an overview of the state-of-the-art techniques in computer-assisted diagnosis systems to predict benign and malignant lesions using computed tomography images. METHODS The research articles published between 1998 and 2020 obtained from various standard databases were considered for preparing the review. The research papers include both conventional as well as deep learning-based systems for liver lesion diagnosis. The paper initially discusses the various hepatic lesions that are identifiable on computed tomography images, then the computer-aided diagnosis systems and their workflow. The conventional and deep learning-based systems are presented in stages wherein the various methods used for preprocessing, liver and lesion segmentation, radiological feature extraction and classification are discussed. CONCLUSION The review suggests the scope for future, work as efficient and effective segmentation methods that work well with diverse images have not been developed. Furthermore, unsupervised and semi-supervised deep learning models were not investigated for liver disease diagnosis in the reviewed papers. Other areas to be explored include image fusion and inclusion of essential clinical features along with the radiological features for better classification accuracy.
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Affiliation(s)
- P Vaidehi Nayantara
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
| | - Surekha Kamath
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
| | - K N Manjunath
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
| | - K V Rajagopal
- Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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17
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Balagourouchetty L, Pragatheeswaran JK, Pottakkat B, Ramkumar G. GoogLeNet-Based Ensemble FCNet Classifier for Focal Liver Lesion Diagnosis. IEEE J Biomed Health Inform 2020; 24:1686-1694. [DOI: 10.1109/jbhi.2019.2942774] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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18
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Chen J, You H, Li K. A review of thyroid gland segmentation and thyroid nodule segmentation methods for medical ultrasound images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 185:105329. [PMID: 31955006 DOI: 10.1016/j.cmpb.2020.105329] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 01/08/2020] [Accepted: 01/08/2020] [Indexed: 05/07/2023]
Abstract
Background and objective Thyroid image segmentation is an indispensable part in computer-aided diagnosis systems and medical image diagnoses of thyroid diseases. There have been dozens of studies on thyroid gland segmentation and thyroid nodule segmentation in ultrasound images. The aim of this work is to categorize and review the thyroid gland segmentation and thyroid nodule segmentation methods in medical ultrasound. Methods This work proposes a categorization approach of thyroid gland segmentation and thyroid nodule segmentation methods according to the theoretical bases of segmentation methods. The segmentation methods are categorized into four groups, including contour and shape based methods, region based methods, machine and deep learning methods and hybrid methods. The representative articles are reviewed with detailed descriptions of methods and analyses of correlations between methods. The evaluation metrics for the reviewed segmentation methods are named uniformly in this work. The segmentation performance results using the uniformly named evaluation metrics are compared. Results After careful investigation, 28 representative papers are selected for comprehensive analyses and comparisons in this review. The dominant thyroid gland segmentation methods are machine and deep learning methods. The training of massive data makes these models have better segmentation performance and robustness. But deep learning models usually require plenty of marked training data and long training time. For thyroid nodule segmentation, the most common methods are contour and shape based methods, which have good segmentation performance. However, most of them are tested on small datasets. Conclusions Based on the comprehensive consideration of application scenario, image features, method practicability and segmentation performance, the appropriate segmentation method for specific situation can be selected. Furthermore, several limitations of current thyroid ultrasound image segmentation methods are presented, which may be overcome in future studies, such as the segmentation of pathological or abnormal thyroid glands, identification of the specific nodular diseases, and the standard thyroid ultrasound image datasets.
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Affiliation(s)
- Junying Chen
- School of Software Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.
| | - Haijun You
- School of Software Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.
| | - Kai Li
- Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510630, China.
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Khan AA, Narejo GB. Analysis of Abdominal Computed Tomography Images for Automatic Liver Cancer Diagnosis Using Image Processing Algorithm. Curr Med Imaging 2019; 15:972-982. [DOI: 10.2174/1573405615666190716122040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 04/23/2019] [Accepted: 06/13/2019] [Indexed: 01/12/2023]
Abstract
Background:
The application of image processing algorithms for medical image analysis
has been found effectual in the past years. Imaging techniques provide assistance to the radiologists
and physicians for the diagnosis of abnormalities in different organs.
Objective:
The proposed algorithm is designed for automatic computer-aided diagnosis of liver
cancer from low contrast CT images. The idea expressed in this article is to classify the malignancy
of the liver tumor ahead of liver segmentation and to locate HCC burden on the liver.
Methods:
A novel Fuzzy Linguistic Constant (FLC) is designed for image enhancement. To classify
the enhanced liver image as cancerous or non-cancerous, fuzzy membership function is applied.
The extracted features are assessed for malignancy and benignancy using the structural similarity
index. The malignant CT image is further processed for automatic tumor segmentation and grading
by applying morphological image processing techniques.
Results:
The validity of the concept is verified on a dataset of 179 clinical cases which consist of
98 benign and 81 malignant liver tumors. Classification accuracy of 98.3% is achieved by Support
Vector Machine (SVM). The proposed method has the ability to automatically segment the tumor
with an improved detection rate of 78% and a precision value of 0.6.
Conclusion:
The algorithm design offers an efficient tool to the radiologist in classifying the malignant
cases from benign cases. The CAD system allows automatic segmentation of tumor and locates
tumor burden on the liver. The methodology adopted can aid medical practitioners in tumor
diagnosis and surgery planning.
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Affiliation(s)
- Ayesha Adil Khan
- Department of Electronics Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Ghous Bakhsh Narejo
- Department of Electronics Engineering, NED University of Engineering & Technology, Karachi, Pakistan
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20
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Yoon Y, Hwang T, Choi H, Lee H. Classification of radiographic lung pattern based on texture analysis and machine learning. J Vet Sci 2019; 20:e44. [PMID: 31364328 PMCID: PMC6669202 DOI: 10.4142/jvs.2019.20.e44] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 05/08/2019] [Accepted: 07/05/2019] [Indexed: 11/20/2022] Open
Abstract
This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstructured interstitial) were obtained from 512 thoracic radiographs of 252 dogs and 65 cats. Forty-four texture parameters based on eight methods of texture analysis (first-order statistics, spatial gray-level-dependence matrices, gray-level-difference statistics, gray-level run length image statistics, neighborhood gray-tone difference matrices, fractal dimension texture analysis, Fourier power spectrum, and Law's texture energy measures) were used to extract textural features from the ROIs. The texture parameters of each lung pattern were compared and used for training and testing of artificial neural networks. Classification performance was evaluated by calculating accuracy and the area under the receiver operating characteristic curve (AUC). Forty texture parameters showed significant differences between the lung patterns. The accuracy of lung pattern classification was 99.1% in the training dataset and 91.9% in the testing dataset. The AUCs were above 0.98 in the training set and above 0.92 in the testing dataset. Texture analysis and machine learning algorithms may potentially facilitate the evaluation of medical images.
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Affiliation(s)
- Youngmin Yoon
- Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Korea
| | - Taesung Hwang
- Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Korea
| | - Hojung Choi
- College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Korea
| | - Heechun Lee
- Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Korea.
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21
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22
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Das A, Das P, Panda SS, Sabut S. Detection of Liver Cancer Using Modified Fuzzy Clustering and Decision Tree Classifier in CT Images. PATTERN RECOGNITION AND IMAGE ANALYSIS 2019. [DOI: 10.1134/s1054661819020056] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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23
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Das A, Acharya UR, Panda SS, Sabut S. Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques. COGN SYST RES 2019. [DOI: 10.1016/j.cogsys.2018.12.009] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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24
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Automated Classification of Fatty and Normal Liver Ultrasound Images Based on Mutual Information Feature Selection. Ing Rech Biomed 2018. [DOI: 10.1016/j.irbm.2018.09.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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25
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Gangeh MJ, Liu S, Tadayyon H, Czarnota GJ. Computer-Aided Theragnosis Based on Tumor Volumetric Information in Breast Cancer. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2018; 65:1359-1369. [PMID: 29994306 DOI: 10.1109/tuffc.2018.2839714] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
OBJECTIVE A computer-assisted technology has recently been proposed for the assessment of therapeutic responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer (LABC). The system, however, extracted features from individual scans in a tumor irrespective of its relation to the other scans of the same patient, ignoring the volumetric information. This study addresses this problem by introducing a novel engineered texton-based method in order to account for volumetric information in the design of textural descriptors to represent tumor scans. METHODS A noninvasive computer-aided-theragnosis (CAT) system was developed by employing multiparametric QUS spectral and backscatter coefficient maps. The proceeding was composed of two subdictionaries: one built on the "pretreatment" and another on "week " scans, where was 1, 4, or 8. The learned dictionary of each patient was subsequently used to compute the model (histogram of textons) for each scan of the patient. Advanced machine learning techniques including a kernel-based dissimilarity measure to estimate the distances between "pretreatment" and "mid-treatment" scans as an indication of treatment effectiveness, learning from imbalanced data, and supervised learning were subsequently employed on the texton-based features. RESULTS The performance of the CAT system was tested using statistical tests of significance and leave-one-subject-out (LOSO) classification on 56 LABC patients. The proposed texton-based CAT system indicated significant differences in changes between the responding and nonresponding patient populations and achieved high accuracy, sensitivity, and specificity in discriminating between the two patient groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. Specifically, the CAT system achieved the area under curve of 0.81, 0.83, and 0.85 on weeks 1, 4, and 8, respectively. CONCLUSION The proposed texton-based CAT system accounted for the volumetric information in "pretreatment" and "mid-treatment" scans of each patient. It was demonstrated that this attribute of the CAT system could boost its performance compared to the cases that the features were extracted from solely individual scans.
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26
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Yu-Feng Liu L, Liu Y, Zhu H. SMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data. Neuroimage 2018; 175:230-245. [PMID: 29596980 PMCID: PMC6317520 DOI: 10.1016/j.neuroimage.2018.03.040] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 03/14/2018] [Accepted: 03/18/2018] [Indexed: 11/21/2022] Open
Abstract
With the development of advanced imaging techniques, scientists are interested in identifying imaging biomarkers that are related to different subtypes or transitional stages of various cancers, neuropsychiatric diseases, and neurodegenerative diseases, among many others. In this paper, we propose a novel spatial multi-category angle-based classifier (SMAC) for the efficient identification of such imaging biomarkers. The proposed SMAC not only utilizes the spatial structure of high-dimensional imaging data but also handles both binary and multi-category classification problems. We introduce an efficient algorithm based on an alternative direction method of multipliers to solve the large-scale optimization problem for SMAC. Both our simulation and real data experiments demonstrate the usefulness of SMAC.
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Affiliation(s)
- Leo Yu-Feng Liu
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center (LCCC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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27
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Yoon Y, Hwang T, Lee H. Prediction of radiographic abnormalities by the use of bag-of-features and convolutional neural networks. Vet J 2018; 237:43-48. [PMID: 30089544 DOI: 10.1016/j.tvjl.2018.05.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 05/24/2018] [Accepted: 05/25/2018] [Indexed: 11/27/2022]
Abstract
This study evaluated the feasibility of bag-of-features (BOF) and convolutional neural networks (CNN) for computer-aided detection in distinguishing normal from abnormal radiographic findings. Computed thoracic radiographs of dogs were collected. For the purposes of this study, radiographic findings were used to distinguish between normal and abnormal in the following areas: (1) normal cardiac silhouette vs. cardiomegaly, (2) normal lung vs. abnormal lung patterns, (3) normal mediastinal position vs. mediastinal shift, (4) normal pleural space vs. pleural effusion, and (5) normal pleural space vs. pneumothorax. Images for training and testing the models consisted of ventrodorsal and lateral projection images of the same scale. The number of images used for each finding are as follow: 3142 for cardiomegaly (1571 normal and 1571 abnormal from 1143 dogs), 2086 for lung pattern (1043 normal and 1043 abnormal from 1247 dogs), 892 for mediastinal shift (446 normal and 446 abnormal from 387 dogs), 940 for pleural effusion (470 normal and 470 abnormal from 284 dogs), and 78 for pneumothorax (39 normal and 39 abnormal from 61 dogs). All data samples were divided so that 60% would be used for training the algorithms and 40% for testing the two models. The performance of the classifiers was evaluated by calculating the accuracy, sensitivity and specificity. The accuracy of both models ranged from 79.6% to 96.9% in the testing set. CNN showed higher accuracy (CNN; 92.9-96.9% and BOF; 79.6-96.9%) and sensitivity (CNN; 92.1-100% and BOF; 74.1-94.8%) than BOF. In conclusion, both BOF and CNN have potential to be useful for improving work efficiency by double reading.
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Affiliation(s)
- Y Yoon
- Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 30488, South Korea
| | - T Hwang
- Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 30488, South Korea
| | - H Lee
- Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 30488, South Korea.
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28
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Vivanti R, Joskowicz L, Lev-Cohain N, Ephrat A, Sosna J. Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies. Med Biol Eng Comput 2018. [PMID: 29524116 DOI: 10.1007/s11517-018-1803-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Radiological longitudinal follow-up of tumors in CT scans is essential for disease assessment and liver tumor therapy. Currently, most tumor size measurements follow the RECIST guidelines, which can be off by as much as 50%. True volumetric measurements are more accurate but require manual delineation, which is time-consuming and user-dependent. We present a convolutional neural networks (CNN) based method for robust automatic liver tumor delineation in longitudinal CT studies that uses both global and patient specific CNNs trained on a small database of delineated images. The inputs are the baseline scan and the tumor delineation, a follow-up scan, and a liver tumor global CNN voxel classifier built from radiologist-validated liver tumor delineations. The outputs are the tumor delineations in the follow-up CT scan. The baseline scan tumor delineation serves as a high-quality prior for the tumor characterization in the follow-up scans. It is used to evaluate the global CNN performance on the new case and to reliably predict failures of the global CNN on the follow-up scan. High-scoring cases are segmented with a global CNN; low-scoring cases, which are predicted to be failures of the global CNN, are segmented with a patient-specific CNN built from the baseline scan. Our experimental results on 222 tumors from 31 patients yield an average overlap error of 17% (std = 11.2) and surface distance of 2.1 mm (std = 1.8), far better than stand-alone segmentation. Importantly, the robustness of our method improved from 67% for stand-alone global CNN segmentation to 100%. Unlike other medical imaging deep learning approaches, which require large annotated training datasets, our method exploits the follow-up framework to yield accurate tumor tracking and failure detection and correction with a small training dataset. Graphical abstract Flow diagram of the proposed method. In the offline mode (orange), a global CNN is trained as a voxel classifier to segment liver tumor as in [31]. The online mode (blue) is used for each new case. The input is baseline scan with delineation and the follow-up CT scan to be segmented. The main novelty is the ability to predict failures by trying the system on the baseline scan and the ability to correct them using the patient-specific CNN.
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Affiliation(s)
- Refael Vivanti
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Naama Lev-Cohain
- Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Ariel Ephrat
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jacob Sosna
- Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
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Chakraborty S, Chatterjee S, Ashour AS, Mali K, Dey N. Intelligent Computing in Medical Imaging. ADVANCEMENTS IN APPLIED METAHEURISTIC COMPUTING 2018. [DOI: 10.4018/978-1-5225-4151-6.ch006] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Biomedical imaging is considered main procedure to acquire valuable physical information about the human body and some other biological species. It produces specialized images of different parts of the biological species for clinical analysis. It assimilates various specialized domains including nuclear medicine, radiological imaging, Positron emission tomography (PET), and microscopy. From the early discovery of X-rays, progress in biomedical imaging continued resulting in highly sophisticated medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, Computed Tomography (CT), and lungs monitoring. These biomedical imaging techniques assist physicians for faster and accurate analysis and treatment. The present chapter discussed the impact of intelligent computing methods for biomedical image analysis and healthcare. Different Artificial Intelligence (AI) based automated biomedical image analysis are considered. Different approaches are discussed including the AI ability to resolve various medical imaging problems. It also introduced the popular AI procedures that employed to solve some special problems in medicine. Artificial Neural Network (ANN) and support vector machine (SVM) are active to classify different types of images from various imaging modalities. Different diagnostic analysis, such as mammogram analysis, MRI brain image analysis, CT images, PET images, and bone/retinal analysis using ANN, feed-forward back propagation ANN, probabilistic ANN, and extreme learning machine continuously. Various optimization techniques of ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and other bio-inspired procedures are also frequently conducted for feature extraction/selection and classification. The advantages and disadvantages of some AI approaches are discussed in the present chapter along with some suggested future research perspectives.
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30
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Thomaz RDL, Carneiro PC, Bonin JE, Macedo TAA, Patrocinio AC, Soares AB. Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma. Med Biol Eng Comput 2017; 56:817-832. [PMID: 29034407 DOI: 10.1007/s11517-017-1736-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 10/04/2017] [Indexed: 11/24/2022]
Abstract
Detection of early hepatocellular carcinoma (HCC) is responsible for increasing survival rates in up to 40%. One-class classifiers can be used for modeling early HCC in multidetector computed tomography (MDCT), but demand the specific knowledge pertaining to the set of features that best describes the target class. Although the literature outlines several features for characterizing liver lesions, it is unclear which is most relevant for describing early HCC. In this paper, we introduce an unconstrained GA feature selection algorithm based on a multi-objective Mahalanobis fitness function to improve the classification performance for early HCC. We compared our approach to a constrained Mahalanobis function and two other unconstrained functions using Welch's t-test and Gaussian Data Descriptors. The performance of each fitness function was evaluated by cross-validating a one-class SVM. The results show that the proposed multi-objective Mahalanobis fitness function is capable of significantly reducing data dimensionality (96.4%) and improving one-class classification of early HCC (0.84 AUC). Furthermore, the results provide strong evidence that intensity features extracted at the arterial to portal and arterial to equilibrium phases are important for classifying early HCC.
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Affiliation(s)
- Ricardo de Lima Thomaz
- Biomedical Engineering Lab, Faculty of Electrical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila 2121, Uberlândia, MG, 38408-100, Brazil.
| | - Pedro Cunha Carneiro
- Biomedical Engineering Lab, Faculty of Electrical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila 2121, Uberlândia, MG, 38408-100, Brazil
| | - João Eliton Bonin
- Department of Radiology, General Hospital of Uberlândia, Federal University of Uberlândia, Av. Pará 1720, Uberlândia, MG, 38405-320, Brazil
| | - Túlio Augusto Alves Macedo
- Department of Radiology, General Hospital of Uberlândia, Federal University of Uberlândia, Av. Pará 1720, Uberlândia, MG, 38405-320, Brazil
| | - Ana Claudia Patrocinio
- Biomedical Engineering Lab, Faculty of Electrical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila 2121, Uberlândia, MG, 38408-100, Brazil
| | - Alcimar Barbosa Soares
- Biomedical Engineering Lab, Faculty of Electrical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila 2121, Uberlândia, MG, 38408-100, Brazil
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Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies. Int J Comput Assist Radiol Surg 2017; 12:1945-1957. [PMID: 28856515 DOI: 10.1007/s11548-017-1660-z] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 08/14/2017] [Indexed: 02/07/2023]
Abstract
PURPOSE Radiological longitudinal follow-up of liver tumors in CT scans is the standard of care for disease progression assessment and for liver tumor therapy. Finding new tumors in the follow-up scan is essential to determine malignancy, to evaluate the total tumor burden, and to determine treatment efficacy. Since new tumors are typically small, they may be missed by examining radiologists. METHODS We describe a new method for the automatic detection and segmentation of new tumors in longitudinal liver CT studies and for liver tumors burden quantification. Its inputs are the baseline and follow-up CT scans, the baseline tumors delineation, and a tumor appearance prior model. Its outputs are the new tumors segmentations in the follow-up scan, the tumor burden quantification in both scans, and the tumor burden change. Our method is the first comprehensive method that is explicitly designed to find new liver tumors. It integrates information from the scans, the baseline known tumors delineations, and a tumor appearance prior model in the form of a global convolutional neural network classifier. Unlike other deep learning-based methods, it does not require large tagged training sets. RESULTS Our experimental results on 246 tumors, of which 97 were new tumors, from 37 longitudinal liver CT studies with radiologist approved ground-truth segmentations, yields a true positive new tumors detection rate of 86 versus 72% with stand-alone detection, and a tumor burden volume overlap error of 16%. CONCLUSIONS New tumors detection and tumor burden volumetry are important for diagnosis and treatment. Our new method enables a simplified radiologist-friendly workflow that is potentially more accurate and reliable than the existing one by automatically and accurately following known tumors and detecting new tumors in the follow-up scan.
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Chang CC, Chen HH, Chang YC, Yang MY, Lo CM, Ko WC, Lee YF, Liu KL, Chang RF. Computer-aided diagnosis of liver tumors on computed tomography images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 145:45-51. [PMID: 28552125 DOI: 10.1016/j.cmpb.2017.04.008] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Revised: 02/19/2017] [Accepted: 04/12/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images. METHODS A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation. RESULTS The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713. CONCLUSIONS Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system.
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Affiliation(s)
- Chin-Chen Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Hong-Hao Chen
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Ming-Yang Yang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Chung-Ming Lo
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Wei-Chun Ko
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yee-Fan Lee
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Kao-Lang Liu
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
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Nogueira MA, Abreu PH, Martins P, Machado P, Duarte H, Santos J. An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images. BMC Med Imaging 2017; 17:13. [PMID: 28193201 PMCID: PMC5307785 DOI: 10.1186/s12880-017-0181-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 01/15/2017] [Indexed: 11/10/2022] Open
Abstract
Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes.
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Affiliation(s)
- Mariana A Nogueira
- CISUC - Department of Informatics Engineering - University of Coimbra, - Pólo II Pinhal de Marrocos, Coimbra, 3030-290, Portugal
| | - Pedro H Abreu
- CISUC - Department of Informatics Engineering - University of Coimbra, - Pólo II Pinhal de Marrocos, Coimbra, 3030-290, Portugal.
| | - Pedro Martins
- CISUC - Department of Informatics Engineering - University of Coimbra, - Pólo II Pinhal de Marrocos, Coimbra, 3030-290, Portugal
| | - Penousal Machado
- CISUC - Department of Informatics Engineering - University of Coimbra, - Pólo II Pinhal de Marrocos, Coimbra, 3030-290, Portugal
| | - Hugo Duarte
- IPO-Porto Research Centre (CI-IPOP), Rua Dr. António Bernardino de Almeida, Porto, 4200-072, Portugal
| | - João Santos
- IPO-Porto Research Centre (CI-IPOP), Rua Dr. António Bernardino de Almeida, Porto, 4200-072, Portugal
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Mayrhofer-Reinhartshuber M, Ahammer H. Pyramidal fractal dimension for high resolution images. CHAOS (WOODBURY, N.Y.) 2016; 26:073109. [PMID: 27475069 DOI: 10.1063/1.4958709] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Fractal analysis (FA) should be able to yield reliable and fast results for high-resolution digital images to be applicable in fields that require immediate outcomes. Triggered by an efficient implementation of FA for binary images, we present three new approaches for fractal dimension (D) estimation of images that utilize image pyramids, namely, the pyramid triangular prism, the pyramid gradient, and the pyramid differences method (PTPM, PGM, PDM). We evaluated the performance of the three new and five standard techniques when applied to images with sizes up to 8192 × 8192 pixels. By using artificial fractal images created by three different generator models as ground truth, we determined the scale ranges with minimum deviations between estimation and theory. All pyramidal methods (PM) resulted in reasonable D values for images of all generator models. Especially, for images with sizes ≥1024×1024 pixels, the PMs are superior to the investigated standard approaches in terms of accuracy and computation time. A measure for the possibility to differentiate images with different intrinsic D values did show not only that the PMs are well suited for all investigated image sizes, and preferable to standard methods especially for larger images, but also that results of standard D estimation techniques are strongly influenced by the image size. Fastest results were obtained with the PDM and PGM, followed by the PTPM. In terms of absolute D values best performing standard methods were magnitudes slower than the PMs. Concluding, the new PMs yield high quality results in short computation times and are therefore eligible methods for fast FA of high-resolution images.
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Affiliation(s)
| | - Helmut Ahammer
- Institute of Biophysics, Center for Physiological Medicine, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
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Parekh V, Jacobs MA. Radiomics: a new application from established techniques. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2016; 1:207-226. [PMID: 28042608 PMCID: PMC5193485 DOI: 10.1080/23808993.2016.1164013] [Citation(s) in RCA: 217] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.
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Affiliation(s)
- Vishwa Parekh
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Department of Computer Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
| | - Michael A. Jacobs
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
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Sethi G, Saini BS. Computer aided diagnosis system for abdomen diseases in computed tomography images. Biocybern Biomed Eng 2016. [DOI: 10.1016/j.bbe.2015.10.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Echegaray S, Gevaert O, Shah R, Kamaya A, Louie J, Kothary N, Napel S. Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma. J Med Imaging (Bellingham) 2015; 2:041011. [PMID: 26587549 DOI: 10.1117/1.jmi.2.4.041011] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 10/14/2015] [Indexed: 12/19/2022] Open
Abstract
The purpose of this study is to investigate the utility of obtaining "core samples" of regions in CT volume scans for extraction of radiomic features. We asked four readers to outline tumors in three representative slices from each phase of multiphasic liver CT images taken from 29 patients (1128 segmentations) with hepatocellular carcinoma. Core samples were obtained by automatically tracing the maximal circle inscribed in the outlines. Image features describing the intensity, texture, shape, and margin were used to describe the segmented lesion. We calculated the intraclass correlation between the features extracted from the readers' segmentations and their core samples to characterize robustness to segmentation between readers, and between human-based segmentation and core sampling. We conclude that despite the high interreader variability in manually delineating the tumor (average overlap of 43% across all readers), certain features such as intensity and texture features are robust to segmentation. More importantly, this same subset of features can be obtained from the core samples, providing as much information as detailed segmentation while being simpler and faster to obtain.
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Affiliation(s)
- Sebastian Echegaray
- Stanford University , Department of Electrical Engineering, 650 Serra Mall, Stanford, California 94305, United States
| | - Olivier Gevaert
- Stanford University , Department of Radiology, 650 Serra Mall, Stanford, California 94305, United States
| | - Rajesh Shah
- Stanford University , Department of Radiology, 650 Serra Mall, Stanford, California 94305, United States
| | - Aya Kamaya
- Stanford University , Department of Radiology, 650 Serra Mall, Stanford, California 94305, United States
| | - John Louie
- Stanford University , Department of Radiology, 650 Serra Mall, Stanford, California 94305, United States
| | - Nishita Kothary
- Stanford University , Department of Radiology, 650 Serra Mall, Stanford, California 94305, United States
| | - Sandy Napel
- Stanford University , Department of Radiology, 650 Serra Mall, Stanford, California 94305, United States
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Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2015; 38:671-88. [DOI: 10.1007/s13246-015-0389-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2014] [Accepted: 10/19/2015] [Indexed: 10/22/2022]
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MOOKIAH MUTHURAMAKRISHNAN, TAN JENHONG, CHUA CHUAKUANG, NG EYK, LAUDE AUGUSTINUS, TONG LOUIS. AUTOMATED CHARACTERIZATION AND DETECTION OF DIABETIC RETINOPATHY USING TEXTURE MEASURES. J MECH MED BIOL 2015. [DOI: 10.1142/s0219519415500451] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The chronic and uncontrolled diabetes mellitus (DM) damages the retinal blood vessels leading to diabetic retinopathy (DR). The advanced stage of DR leads to loss of vision and subsequently blindness. The morphological changes during the progression of DR can be diagnosed using digital fundus images. The pathological changes in the retina influence the variations in pixel patterns which can be quantified using texture measures. In this paper, we have explored different texture measures namely statistical moments, gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), local binary pattern (LBP), laws mask energy (LME), fractal dimension (FD), fourier spectrum (FS) and Gabor wavelet to characterize and classify the normal and DR classes. We have tabulated 109 texture parameters for the normal and DR classes. Further, these features were subjected to empirical receiver operating characteristic (ROC) based ranking to select optimal feature set. The ranked nested features were fed to the support vector machine (SVM) classifier with different kernel functions to evaluate the highest performance measure using the least number of features to discriminate normal and DR classes. Our proposed system was evaluated using two different databases Kasturba Medical College Hospital (KMCH) and Tan Tock Seng Hospital (TTSH), each with 340 images (170 normal and 170 DR). We have also formulated an integrated index called as diabetic retinopathy risk index (DRRI) using selected texture features to discriminate normal and DR classes using single number. The proposed frame work can be used to help the clinicians and also for mass DR screening programs.
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Affiliation(s)
| | - JEN HONG TAN
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - CHUA KUANG CHUA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - E. Y. K. NG
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - AUGUSTINUS LAUDE
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - LOUIS TONG
- Singapore National Eye Center, Singapore 168751, Singapore
- Ocular Surface Research Group, Singapore Eye Research Institute, Singapore 168751, Singapore
- Duke-NUS Graduate Medical School, Singapore 169857, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
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Lerski RA, de Certaines JD, Duda D, Klonowski W, Yang G, Coatrieux JL, Azzabou N, Eliat PA. Application of texture analysis to muscle MRI: 2 – technical recommendations. ACTA ACUST UNITED AC 2015. [DOI: 10.1140/epjnbp/s40366-015-0018-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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MRI Texture Analysis for Differentiation Between Healthy and Golden Retriever Muscular Dystrophy Dogs at Different Phases of Disease Evolution. COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT 2015. [DOI: 10.1007/978-3-319-24369-6_21] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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43
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Anter AM, Abu El Souod M, Azar AT, Hassanien AE. A Hybrid Approach to Diagnosis of Hepatic Tumors in Computed Tomography Images. ACTA ACUST UNITED AC 2014. [DOI: 10.4018/ijrsda.2014070103] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Liver cancer is one of the most popular cancer diseases and causes a large amount of death every year, can be reduced by early detection and diagnosis. Computer-aided liver analysis can help in the early detection and diagnosis of liver cancer. In this paper, enhancement and segmentation process is applied to increase the computation and focus on liver parenchyma. This parenchyma also segmented using Watershed and Region Growing algorithms to extract liver tumors. These tumors will be analyzed and characterized to distinguish between hemangioma (benign) and hepatocellular (malignant) tumors using Local Binary Pattern (LBP), Gray Level Co-occurrence matrix (GLCM), Fractal Dimension (FD) and feature fusion technique is applied to maximize and enhance the performance of the classifier rate. The authors review different methods for liver segmentation and abnormality classification. An attempt was made to combine the individual scores from different techniques in order to compensate their individual weaknesses and to preserve their strength. The authors present and exhaustively evaluate algorithms using computer vision techniques. The experimental results based on confusion matrix and kappa coefficient show that the higher accuracy is obtained of automatic agreement classification and suggest that the developed CAD system has great potential and promise in the automatic diagnosis of both benign and malignant tumors of liver.
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Affiliation(s)
- Ahmed M. Anter
- Faculty of Computers and Information, CS Department, Mansoura University, Mansoura, Egypt & Scientific Research Group in Egypt (SRGE), Mansoura, Egypt
| | - Mohamed Abu El Souod
- Faculty of Computers and Information, CS Department, Mansoura University, Mansoura, Egypt
| | - Ahmad Taher Azar
- Faculty of Computers and Information, Benha University, Benha, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Information, Cairo University, Cairo, Egypt and Scientific Research Group in Egypt (SRGE), Mansoura, Egypt
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Liu J, Wang S, Linguraru MG, Yao J, Summers RM. Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT. Med Image Anal 2014; 18:725-39. [PMID: 24835180 DOI: 10.1016/j.media.2014.04.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2013] [Revised: 03/31/2014] [Accepted: 04/02/2014] [Indexed: 10/25/2022]
Abstract
Accurate automated segmentation and detection of ovarian cancer metastases may improve the diagnosis and prognosis of women with ovarian cancer. In this paper, we focus on an important subset of ovarian cancer metastases that spread to the surface of the liver and spleen. Automated ovarian cancer metastasis detection and segmentation are very challenging problems to solve. These metastases have a wide variety of shapes and intensity values similar to that of the liver, spleen and adjacent soft tissues. To address these challenges, this paper presents a variational approach, called tumor sensitive matching flow (TSMF), to detect and segment perihepatic and perisplenic ovarian cancer metastases. TSMF is an image motion field that only highlights metastasis-caused deformation on the surface of liver and spleen while dampening all other image motion between the patient image and the atlas image. It provides several benefits: (1) juxtaposing the roles of image matching and metastasis classification within a variational framework; (2) only requiring a small set of features from a few patient images to train a metastasis-likelihood function for classification; and (3) dynamically creating shape priors for geodesic active contour (GAC) to prevent inaccurate metastasis segmentation. We compared the TSMF to an organ surface partition (OSP) baseline approach. At a false positive rate of 2 per patient, the sensitivities of TSMF and OSP were 87% and 17% (p<0.001), respectively. In a comparison of the segmentations conducted using TSMF-constrained GAC and conventional GAC, the volume overlap rates were 73 ± 9% and 46 ± 26% (p<0.001) and average surface distances were 2.4 ± 1.2 mm and 7.0 ± 6.0 mm (p<0.001), respectively. These encouraging results demonstrate that TSMF could accurately detect and segment ovarian cancer metastases.
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Affiliation(s)
- Jianfei Liu
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Shijun Wang
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, USA; Departments of Radiology and Pediatrics, School of Medicine and Health Sciences, George Washington University, Washington, DC, USA
| | - Jianhua Yao
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Ronald M Summers
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
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Duda D, Kretowski M, Mathieu R, de Crevoisier R, Bezy-Wendling J. Multi-Image Texture Analysis in Classification of Prostatic Tissues from MRI. Preliminary Results. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2014. [DOI: 10.1007/978-3-319-06593-9_13] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Dankerl P, Cavallaro A, Tsymbal A, Costa MJ, Suehling M, Janka R, Uder M, Hammon M. A retrieval-based computer-aided diagnosis system for the characterization of liver lesions in CT scans. Acad Radiol 2013; 20:1526-34. [PMID: 24200479 DOI: 10.1016/j.acra.2013.09.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 08/30/2013] [Accepted: 09/01/2013] [Indexed: 11/26/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate a computer-aided diagnosis (CADx) system for the characterization of liver lesions in computed tomography (CT) scans. The stand-alone predictive performance of the CADx system was assessed and compared to that of three radiologists who were provided with the same amount of image information to which the CADx system had access. MATERIALS AND METHODS The CADx system operates as an image search engine exploiting texture analysis of liver lesion image data for the lesion in question and lesions from a database. A region of interest drawn around an indeterminate liver lesion is used as input query. The CADx system retrieves lesions of similar histology (benign/malignant), density (hypodense/hyperdense), or type (cyst/hemangioma/metastasis). The system's performance was evaluated with leave-one-patient-out receiver operating characteristic area under the curve on 685 CT scans from 372 patients that contained 2325 liver lesions (193 <1 cm(3)). Sensitivity, specificity, and positive and negative predictive values were evaluated separately for subcentimeter lesions. Results were compared to those of three radiologists who rated 83 liver lesions (20 hemangiomas, 20 metastases, 20 cysts, 20 hepatocellular carcinomas, and 3 focal nodular hyperplasias) displaying only the liver. RESULTS The CADx system's leave-one-patient-out receiver operating characteristic area under the curve was 97.1% for density, 91.4% for histology, and 95.5% for lesion type. For subcentimeter lesions, input of additional semantic information improved the system's performance. The CADx system has been proved to significantly outperform radiologists in discriminating lesion histology and type, provided the radiologists have no access to information other than the image. The radiologists were most reliable in diagnosing hemangioma given the limited image data. CONCLUSIONS The CADx system under study discriminated reliably between various liver lesions, even outperforming radiologists when accessing the same image information and demonstrated promising performance in classifying subcentimeter lesions in particular.
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Lee J, Kim KW, Kim SY, Kim B, Lee SJ, Kim HJ, Lee JS, Lee MG, Song GW, Hwang S, Lee SG. Feasibility of semiautomated MR volumetry using gadoxetic acid-enhanced MRI at hepatobiliary phase for living liver donors. Magn Reson Med 2013; 72:640-5. [PMID: 24151218 DOI: 10.1002/mrm.24964] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 09/03/2013] [Accepted: 09/03/2013] [Indexed: 01/02/2023]
Abstract
PURPOSE To assess the feasibility of semiautomated MR volumetry using gadoxetic acid-enhanced MRI at the hepatobiliary phase compared with manual CT volumetry. METHODS Forty potential live liver donor candidates who underwent MR and CT on the same day, were included in our study. Semiautomated MR volumetry was performed using gadoxetic acid-enhanced MRI at the hepatobiliary phase. We performed the quadratic MR image division for correction of the bias field inhomogeneity. With manual CT volumetry as the reference standard, we calculated the average volume measurement error of the semiautomated MR volumetry. We also calculated the mean of the number and time of the manual editing, edited volume, and total processing time. RESULTS The average volume measurement errors of the semiautomated MR volumetry were 2.35% ± 1.22%. The average values of the numbers of editing, operation times of manual editing, edited volumes, and total processing time for the semiautomated MR volumetry were 1.9 ± 0.6, 8.1 ± 2.7 s, 12.4 ± 8.8 mL, and 11.7 ± 2.9 s, respectively. CONCLUSION Semiautomated liver MR volumetry using hepatobiliary phase gadoxetic acid-enhanced MRI with the quadratic MR image division is a reliable, easy, and fast tool to measure liver volume in potential living liver donors.
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Affiliation(s)
- Jeongjin Lee
- School of Computer Science & Engineering, Soongsil University, Seoul, Korea
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Yang W, Lu Z, Yu M, Huang M, Feng Q, Chen W. Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single- and multiphase contrast-enhanced CT images. J Digit Imaging 2013; 25:708-19. [PMID: 22692772 DOI: 10.1007/s10278-012-9495-1] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
Abstract
This paper is aimed at developing and evaluating a content-based retrieval method for contrast-enhanced liver computed tomographic (CT) images using bag-of-visual-words (BoW) representations of single and multiple phases. The BoW histograms are extracted using the raw intensity as local patch descriptor for each enhance phase by densely sampling the image patches within the liver lesion regions. The distance metric learning algorithms are employed to obtain the semantic similarity on the Hellinger kernel feature map of the BoW histograms. The different visual vocabularies for BoW and learned distance metrics are evaluated in a contrast-enhanced CT image dataset comprised of 189 patients with three types of focal liver lesions, including 87 hepatomas, 62 cysts, and 60 hemangiomas. For each single enhance phase, the mean of average precision (mAP) of BoW representations for retrieval can reach above 90 % which is significantly higher than that of intensity histogram and Gabor filters. Furthermore, the combined BoW representations of the three enhance phases can improve mAP to 94.5 %. These preliminary results demonstrate that the BoW representation is effective and feasible for retrieval of liver lesions in contrast-enhanced CT images.
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Affiliation(s)
- Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
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49
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Liver vasculature refinement with multiple 3D structuring element shapes. Pattern Anal Appl 2013. [DOI: 10.1007/s10044-013-0338-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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50
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Acharya UR, Vinitha Sree S, Mookiah MRK, Yantri R, Molinari F, Zieleźnik W, Małyszek-Tumidajewicz J, Stępień B, Bardales RH, Witkowska A, Suri JS. Diagnosis of Hashimoto's thyroiditis in ultrasound using tissue characterization and pixel classification. Proc Inst Mech Eng H 2013; 227:788-98. [PMID: 23636761 DOI: 10.1177/0954411913483637] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Hashimoto's thyroiditis is the most common type of inflammation of the thyroid gland, and accurate diagnosis of Hashimoto's thyroiditis would be helpful to better manage the disease process and predict thyroid failure. Most of the published computer-based techniques that use ultrasound thyroid images for Hashimoto's thyroiditis diagnosis are limited by lack of procedure standardization because individual investigators use various initial ultrasound settings. This article presents a computer-aided diagnostic technique that uses grayscale features and classifiers to provide a more objective and reproducible classification of normal and Hashimoto's thyroiditis-affected cases. In this paradigm, we extracted grayscale features based on entropy, Gabor wavelet, moments, image texture, and higher order spectra from the 100 normal and 100 Hashimoto's thyroiditis-affected ultrasound thyroid images. Significant features were selected using t-test. The resulting feature vectors were used to build the following three classifiers using tenfold stratified cross validation technique: support vector machine, k-nearest neighbor, and radial basis probabilistic neural network. Our results show that a combination of 12 features coupled with support vector machine classifier with the polynomial kernel of order 1 and linear kernel gives the highest accuracy of 80%, sensitivity of 76%, specificity of 84%, and positive predictive value of 83.3% for the detection of Hashimoto's thyroiditis. The proposed computer-aided diagnostic system uses novel features that have not yet been explored for Hashimoto's thyroiditis diagnosis. Even though the accuracy is only 80%, the presented preliminary results are encouraging to warrant analysis of more such powerful features on larger databases.
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Affiliation(s)
- U R Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
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