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Khan R, Xiao C, Liu Y, Tian J, Chen Z, Su L, Li D, Hassan H, Li H, Xie W, Zhong W, Huang B. Transformative Deep Neural Network Approaches in Kidney Ultrasound Segmentation: Empirical Validation with an Annotated Dataset. Interdiscip Sci 2024; 16:439-454. [PMID: 38413547 DOI: 10.1007/s12539-024-00620-3] [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: 07/04/2023] [Revised: 01/06/2024] [Accepted: 02/05/2024] [Indexed: 02/29/2024]
Abstract
Kidney ultrasound (US) images are primarily employed for diagnosing different renal diseases. Among them, one is renal localization and detection, which can be carried out by segmenting the kidney US images. However, kidney segmentation from US images is challenging due to low contrast, speckle noise, fluid, variations in kidney shape, and modality artifacts. Moreover, well-annotated US datasets for renal segmentation and detection are scarce. This study aims to build a novel, well-annotated dataset containing 44,880 US images. In addition, we propose a novel training scheme that utilizes the encoder and decoder parts of a state-of-the-art segmentation algorithm. In the pre-processing step, pixel intensity normalization improves contrast and facilitates model convergence. The modified encoder-decoder architecture improves pyramid-shaped hole pooling, cascaded multiple-hole convolutions, and batch normalization. The pre-processing step gradually reconstructs spatial information, including the capture of complete object boundaries, and the post-processing module with a concave curvature reduces the false positive rate of the results. We present benchmark findings to validate the quality of the proposed training scheme and dataset. We applied six evaluation metrics and several baseline segmentation approaches to our novel kidney US dataset. Among the evaluated models, DeepLabv3+ performed well and achieved the highest dice, Hausdorff distance 95, accuracy, specificity, average symmetric surface distance, and recall scores of 89.76%, 9.91, 98.14%, 98.83%, 3.03, and 90.68%, respectively. The proposed training strategy aids state-of-the-art segmentation models, resulting in better-segmented predictions. Furthermore, the large, well-annotated kidney US public dataset will serve as a valuable baseline source for future medical image analysis research.
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Affiliation(s)
- Rashid Khan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518188, China
- College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Chuda Xiao
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518188, China
- Wuerzburg Dynamics Inc., Shenzhen, 518188, China
| | - Yang Liu
- Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China
| | - Jinyu Tian
- Wuerzburg Dynamics Inc., Shenzhen, 518188, China
| | - Zhuo Chen
- Wuerzburg Dynamics Inc., Shenzhen, 518188, China
| | - Liyilei Su
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518188, China
- College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Dan Li
- Wuerzburg Dynamics Inc., Shenzhen, 518188, China
| | - Haseeb Hassan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518188, China
| | - Haoyu Li
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518188, China
| | - Weiguo Xie
- Wuerzburg Dynamics Inc., Shenzhen, 518188, China
| | - Wen Zhong
- Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China.
| | - Bingding Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518188, China
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Chen J, Chen R, Chen L, Zhang L, Wang W, Zeng X. Kidney medicine meets computer vision: a bibliometric analysis. Int Urol Nephrol 2024:10.1007/s11255-024-04082-w. [PMID: 38814370 DOI: 10.1007/s11255-024-04082-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 05/16/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND AND OBJECTIVE Rapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine research. METHODS The Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and Vosviewer. RESULTS There was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data modalities. CONCLUSION The present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key sources, leading institutions, and popular topics. Our findings and analysis are expected to provide valuable insights for future research on the use of CV in kidney medicine research.
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Affiliation(s)
- Junren Chen
- Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Rui Chen
- The Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
| | - Liangyin Chen
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Lei Zhang
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Wei Wang
- School of Automation, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China
| | - Xiaoxi Zeng
- Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, Sichuan, China.
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Kellner E, Sekula P, Lipovsek J, Russe M, Horbach H, Schlett CL, Nauck M, Völzke H, Kroencke T, Bette S, Kauczor HU, Keil T, Pischon T, Heid IM, Peters A, Niendorf T, Lieb W, Bamberg F, Büchert M, Reichardt W, Reisert M, Köttgen A. Imaging Markers Derived From MRI-Based Automated Kidney Segmentation. DEUTSCHES ARZTEBLATT INTERNATIONAL 2024; 121:284-290. [PMID: 38530931 DOI: 10.3238/arztebl.m2024.0040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 02/19/2024] [Accepted: 02/19/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Population-wide research on potential new imaging biomarkers of the kidney depends on accurate automated segmentation of the kidney and its compartments (cortex, medulla, and sinus). METHODS We developed a robust deep-learning framework for kidney (sub-)segmentation based on a hierarchical, three-dimensional convolutional neural network (CNN) that was optimized for multiscale problems of combined localization and segmentation. We applied the CNN to abdominal magnetic resonance images from the population-based German National Cohort (NAKO) study. RESULTS There was good to excellent agreement between the model predictions and manual segmentations. The median values for the body-surface normalized total kidney, cortex, medulla, and sinus volumes of 9934 persons were 158, 115, 43, and 24 mL/m2. Distributions of these markers are provided both for the overall study population and for a subgroup of persons without kidney disease or any associated conditions. Multivariable adjusted regression analyses revealed that diabetes, male sex, and a higher estimated glomerular filtration rate (eGFR) are important predictors of higher total and cortical volumes. Each increase of eGFR by one unit (i.e., 1 mL/min per 1.73 m2 body surface area) was associated with a 0.98 mL/m2 increase in total kidney volume, and this association was significant. Volumes were lower in persons with eGFR-defined chronic kidney disease. CONCLUSION The extraction of image-based biomarkers through CNN-based renal sub-segmentation using data from a population-based study yields reliable results, forming a solid foundation for future investigations.
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Affiliation(s)
- Elias Kellner
- Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany; Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Germany; Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine Greifswald, Germany; Institute for Community Medicine, University Medicine Greifswald, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Germany; Centre for Advanced Analytics and Predictive Sciences (CAAPS), University of Augsburg, Germany; Department of Diagnostical and Interventional Radiology, University Hospital Heidelberg, Germany; Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Institute of Clinical Epidemiology and Biometry, University of Würzburg, State Institute of Health I, Bavarian Health and Food Safety Authority, Erlangen, Germany; Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group; Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology Platform, Berlin; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; Chair of Genetic Epidemiology, University of Regensburg, Germany; Institute of Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg; Chair of Epidemiology, Institute for Medical Information Processing, Biometrics, and Epidemiology, Medical Faculty, Ludwig-Maximilians-University Munich; DZHK (German Centre for Cardiovascular Research), Partner Site Munich, Munich Heart Alliance, Munich; DZD (German Centre for Diabetes Research), Neuherberg; Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin; Institute of Epidemiology, Kiel University, Kiel, Germany; Department of Diagnostic and Interventional Radiology, Core Facility MRDAC, University Medical Center Freiburg, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Germany
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Xu T, Zhang XY, Yang N, Jiang F, Chen GQ, Pan XF, Peng YX, Cui XW. A narrative review on the application of artificial intelligence in renal ultrasound. Front Oncol 2024; 13:1252630. [PMID: 38495082 PMCID: PMC10943690 DOI: 10.3389/fonc.2023.1252630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/12/2023] [Indexed: 03/19/2024] Open
Abstract
Kidney disease is a serious public health problem and various kidney diseases could progress to end-stage renal disease. The many complications of end-stage renal disease. have a significant impact on the physical and mental health of patients. Ultrasound can be the test of choice for evaluating the kidney and perirenal tissue as it is real-time, available and non-radioactive. To overcome substantial interobserver variability in renal ultrasound interpretation, artificial intelligence (AI) has the potential to be a new method to help radiologists make clinical decisions. This review introduces the applications of AI in renal ultrasound, including automatic segmentation of the kidney, measurement of the renal volume, prediction of the kidney function, diagnosis of the kidney diseases. The advantages and disadvantages of the applications will also be presented clinicians to conduct research. Additionally, the challenges and future perspectives of AI are discussed.
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Affiliation(s)
- Tong Xu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xian-Ya Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Na Yang
- Department of Ultrasound, Affiliated Hospital of Jilin Medical College, Jilin, China
| | - Fan Jiang
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian, China
| | - Yue-Xiang Peng
- Department of Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Li L, Tan J, Yu L, Li C, Nan H, Zheng S. LSAM: L2-norm self-attention and latent space feature interaction for automatic 3D multi-modal head and neck tumor segmentation. Phys Med Biol 2023; 68:225004. [PMID: 37852283 DOI: 10.1088/1361-6560/ad04a8] [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: 04/11/2023] [Accepted: 10/18/2023] [Indexed: 10/20/2023]
Abstract
Objective.Head and neck (H&N) cancers are prevalent globally, and early and accurate detection is absolutely crucial for timely and effective treatment. However, the segmentation of H&N tumors is challenging due to the similar density of the tumors and surrounding tissues in CT images. While positron emission computed tomography (PET) images provide information about the metabolic activity of the tissue and can distinguish between lesion regions and normal tissue. But they are limited by their low spatial resolution. To fully leverage the complementary information from PET and CT images, we propose a novel and innovative multi-modal tumor segmentation method specifically designed for H&N tumor segmentation.Approach.The proposed novel and innovative multi-modal tumor segmentation network (LSAM) consists of two key learning modules, namely L2-Norm self-attention and latent space feature interaction, which exploit the high sensitivity of PET images and the anatomical information of CT images. These two advanced modules contribute to a powerful 3D segmentation network based on a U-shaped structure. The well-designed segmentation method can integrate complementary features from different modalities at multiple scales, thereby improving the feature interaction between modalities.Main results.We evaluated the proposed method on the public HECKTOR PET-CT dataset, and the experimental results demonstrate that the proposed method convincingly outperforms existing H&N tumor segmentation methods in terms of key evaluation metrics, including DSC (0.8457), Jaccard (0.7756), RVD (0.0938), and HD95 (11.75).Significance.The innovative Self-Attention mechanism based on L2-Norm offers scalability and is effective in reducing the impact of outliers on the performance of the model. And the novel method for multi-scale feature interaction based on Latent Space utilizes the learning process in the encoder phase to achieve the best complementary effects among different modalities.
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Affiliation(s)
- Laquan Li
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China
- School of Science, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China
| | - Jiaxin Tan
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China
| | - Lei Yu
- Emergency Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Chunwen Li
- Emergency Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Hai Nan
- College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, People's Republic of China
| | - Shenhai Zheng
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China
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Peng T, Gu Y, Ruan SJ, Wu QJ, Cai J. Novel Solution for Using Neural Networks for Kidney Boundary Extraction in 2D Ultrasound Data. Biomolecules 2023; 13:1548. [PMID: 37892229 PMCID: PMC10604927 DOI: 10.3390/biom13101548] [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: 07/16/2023] [Revised: 09/30/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
Background and Objective: Kidney ultrasound (US) imaging is a significant imaging modality for evaluating kidney health and is essential for diagnosis, treatment, surgical intervention planning, and follow-up assessments. Kidney US image segmentation consists of extracting useful objects or regions from the total image, which helps determine tissue organization and improve diagnosis. Thus, obtaining accurate kidney segmentation data is an important first step for precisely diagnosing kidney diseases. However, manual delineation of the kidney in US images is complex and tedious in clinical practice. To overcome these challenges, we developed a novel automatic method for US kidney segmentation. Methods: Our method comprises two cascaded steps for US kidney segmentation. The first step utilizes a coarse segmentation procedure based on a deep fusion learning network to roughly segment each input US kidney image. The second step utilizes a refinement procedure to fine-tune the result of the first step by combining an automatic searching polygon tracking method with a machine learning network. In the machine learning network, a suitable and explainable mathematical formula for kidney contours is denoted by basic parameters. Results: Our method is assessed using 1380 trans-abdominal US kidney images obtained from 115 patients. Based on comprehensive comparisons of different noise levels, our method achieves accurate and robust results for kidney segmentation. We use ablation experiments to assess the significance of each component of the method. Compared with state-of-the-art methods, the evaluation metrics of our method are significantly higher. The Dice similarity coefficient (DSC) of our method is 94.6 ± 3.4%, which is higher than those of recent deep learning and hybrid algorithms (89.4 ± 7.1% and 93.7 ± 3.8%, respectively). Conclusions: We develop a coarse-to-refined architecture for the accurate segmentation of US kidney images. It is important to precisely extract kidney contour features because segmentation errors can cause under-dosing of the target or over-dosing of neighboring normal tissues during US-guided brachytherapy. Hence, our method can be used to increase the rigor of kidney US segmentation.
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Affiliation(s)
- Tao Peng
- School of Future Science and Engineering, Soochow University, Suzhou 215006, China
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Yidong Gu
- Department of Medical Ultrasound, Suzhou Municipal Hospital, Suzhou 215000, China;
| | - Shanq-Jang Ruan
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan;
| | - Qingrong Jackie Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA;
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Xiao H, Li L, Liu Q, Zhu X, Zhang Q. Transformers in medical image segmentation: A review. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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Valente S, Morais P, Torres HR, Oliveira B, Buschle LR, Fritz A, Correia-Pinto J, Lima E, Vilaca JL. A Comparative Study of Deep Learning Methods for Multi-Class Semantic Segmentation of 2D Kidney Ultrasound Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083246 DOI: 10.1109/embc40787.2023.10341170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Ultrasound (US) imaging is a widely used medical imaging modality for the diagnosis, monitoring, and surgical planning for kidney conditions. Thus, accurate segmentation of the kidney and internal structures in US images is essential for the assessment of kidney function and the detection of pathological conditions, such as cysts, tumors, and kidney stones. Therefore, there is a need for automated methods that can accurately segment the kidney and internal structures in US images. Over the years, automatic strategies were proposed for such purpose, with deep learning methods achieving the current state-of-the-art results. However, these strategies typically ignore the segmentation of the internal structures of the kidney. Moreover, they were evaluated in different private datasets, hampering the direct comparison of results, and making it difficult to determination the optimal strategy for this task. In this study, we perform a comparative analysis of 7 deep learning networks for the segmentation of the kidney and internal structures (Capsule, Central Echogenic Complex (CEC), Cortex and Medulla) in 2D US images in an open access multi-class kidney US dataset. The dataset includes 514 images, acquired in multiple clinical centers using different US machines and protocols. The dataset contains the annotation of two experts, but 321 images with complete segmentation of all 4 classes were used. Overall, the results demonstrate that the DeepLabV3+ network outperformed the inter-rater variability with a Dice score of 78.0% compared to 75.6% for inter-rater variability. Specifically, DeepLabV3Plus achieved mean Dice scores of 94.2% for the Capsule, 85.8% for the CEC, 62.4% for the Cortex, and 69.6% for the Medulla. These findings suggest the potential of deep learning-based methods in improving the accuracy of kidney segmentation in US images.Clinical Relevance- This study shows the potential of DL for improving accuracy of kidney segmentation in US, leading to increased diagnostic efficiency, and enabling new applications such as computer-aided diagnosis and treatment, ultimately resulting in improved patient outcomes and reduced healthcare costs.1.
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Torres HR, Oliveira B, Fonseca JC, Morais P, Vilaca JL. Dual consistency loss for contour-aware segmentation in medical images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082637 DOI: 10.1109/embc40787.2023.10340931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Medical image segmentation is a paramount task for several clinical applications, namely for the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries. With the development of deep learning, Convolutional Neural Networks (CNN) have become the state-of-the-art for medical image segmentation. However, issues are still raised concerning the precise object boundary delineation, since traditional CNNs can produce non-smooth segmentations with boundary discontinuities. In this work, a U-shaped CNN architecture is proposed to generate both pixel-wise segmentation and probabilistic contour maps of the object to segment, in order to generate reliable segmentations at the object's boundaries. Moreover, since the segmentation and contour maps must be inherently related to each other, a dual consistency loss that relates the two outputs of the network is proposed. Thus, the network is enforced to consistently learn the segmentation and contour delineation tasks during the training. The proposed method was applied and validated on a public dataset of cardiac 3D ultrasound images of the left ventricle. The results obtained showed the good performance of the method and its applicability for the cardiac dataset, showing its potential to be used in clinical practice for medical image segmentation.Clinical Relevance- The proposed network with dual consistency loss scheme can improve the performance of state-of-the-art CNNs for medical image segmentation, proving its value to be applied for computer-aided diagnosis.
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Badawy M, Almars AM, Balaha HM, Shehata M, Qaraad M, Elhosseini M. A two-stage renal disease classification based on transfer learning with hyperparameters optimization. Front Med (Lausanne) 2023; 10:1106717. [PMID: 37089598 PMCID: PMC10113505 DOI: 10.3389/fmed.2023.1106717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 03/14/2023] [Indexed: 04/09/2023] Open
Abstract
Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which affect anywhere from 1 to 15% of the global population and thus; considered one of the leading causes of chronic kidney diseases (CKD). In addition to kidney stones, renal cancer is the tenth most prevalent type of cancer, accounting for 2.5% of all cancers. Artificial intelligence (AI) in medical systems can assist radiologists and other healthcare professionals in diagnosing different renal diseases (RD) with high reliability. This study proposes an AI-based transfer learning framework to detect RD at an early stage. The framework presented on CT scans and images from microscopic histopathological examinations will help automatically and accurately classify patients with RD using convolutional neural network (CNN), pre-trained models, and an optimization algorithm on images. This study used the pre-trained CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, and NASNetMobile. In addition, the Sparrow search algorithm (SpaSA) is used to enhance the pre-trained model's performance using the best configuration. Two datasets were used, the first dataset are four classes: cyst, normal, stone, and tumor. In case of the latter, there are five categories within the second dataset that relate to the severity of the tumor: Grade 0, Grade 1, Grade 2, Grade 3, and Grade 4. DenseNet201 and MobileNet pre-trained models are the best for the four-classes dataset compared to others. Besides, the SGD Nesterov parameters optimizer is recommended by three models, while two models only recommend AdaGrad and AdaMax. Among the pre-trained models for the five-class dataset, DenseNet201 and Xception are the best. Experimental results prove the superiority of the proposed framework over other state-of-the-art classification models. The proposed framework records an accuracy of 99.98% (four classes) and 100% (five classes).
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Affiliation(s)
- Mahmoud Badawy
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
- Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah, Saudi Arabia
| | - Abdulqader M Almars
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
| | - Hossam Magdy Balaha
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
- Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, United States
| | - Mohamed Shehata
- Department of Computer Science and Engineering, Speed School of Engineering, University of Louisville, Louisville, KY, United States
| | - Mohammed Qaraad
- Department of Computer Science, Faculty of Science, Amran University, Amran, Yemen
- TIMS, Faculty of Science, Abdelmalek Essaadi University, Tetouan, Morocco
| | - Mostafa Elhosseini
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
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AlHmoud IW, Walmer RW, Kavanagh K, Chang EH, Johnson KA, Bikdash M. Classifying Kidney Disease in a Vervet Model Using Spatially Encoded Contrast-Enhanced Ultrasound Perfusion Parameters. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:761-772. [PMID: 36463005 PMCID: PMC11217529 DOI: 10.1016/j.ultrasmedbio.2022.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/14/2022] [Accepted: 10/18/2022] [Indexed: 06/01/2023]
Abstract
Early stages of diabetic kidney disease (DKD) are difficult to diagnose in patients with type 2 diabetes. This work was aimed at identifying contrast-enhanced ultrasound (CEUS) perfusion parameters, a microcirculatory biomarker indicative of early DKD progression. CEUS kidney flash-replenishment data were acquired in control, insulin resistant and diabetic vervet monkeys (N = 16). By use of a mono-exponential model, time-intensity curve parameters related to blood volume (A), velocity (β) and flow rate (perfusion index [PI]) were extracted from 10 concentric kidney layers to study spatial perfusion patterns that could serve as strong indicators of disease. Mean squared error (MSE) was used to assess model performance. Features calculated from the perfusion parameters were inputs for the linear regression models to determine which features could distinguish between cohorts. The mono-exponential model performed well, with average MSEs (±standard deviation) of 0.0254 (±0.0210), 0.0321 (±0.0242) and 0.0287 (±0.0130) for the control, insulin resistant and diabetic cohorts, respectively. Perfusion index features, with blood pressure, were the best classifiers between cohorts (p < 0.05). CEUS has the potential to detect early microvascular changes, providing insight into disease-related structural changes in the kidney. The sensitivity of this technique should be explored further by assessing various stages of DKD.
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Affiliation(s)
- Issa W AlHmoud
- Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, North Carolina, USA
| | - Rachel W Walmer
- Joint Department of Biomedical Engineering, North Carolina State University and the University of North Carolina at Chapel Hill, Raleigh, North Carolina, USA
| | - Kylie Kavanagh
- Department of Pathology, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA; College of Health and Medicine, University of Tasmania, Hobart, Tasmania, Australia
| | - Emily H Chang
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kennita A Johnson
- Joint Department of Biomedical Engineering, North Carolina State University and the University of North Carolina at Chapel Hill, Raleigh, North Carolina, USA.
| | - Marwan Bikdash
- Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, North Carolina, USA
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12
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Alex DM, Abraham Chandy D, Hepzibah Christinal A, Singh A, Pushkaran M. YSegNet: a novel deep learning network for kidney segmentation in 2D ultrasound images. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07624-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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13
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Pandey M, Gupta A. Tumorous kidney segmentation in abdominal CT images using active contour and 3D-UNet. Ir J Med Sci 2022:10.1007/s11845-022-03113-8. [PMID: 35930139 DOI: 10.1007/s11845-022-03113-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 07/20/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND AND PURPOSE The precise segmentation of the kidneys in computed tomography (CT) images is vital in urology for diagnosis, treatment, and surgical planning. Medical experts can get assistance through segmentation, as it provides information about kidney malformations in terms of shape and size. Manual segmentation is slow, tedious, and not reproducible. An automatic computer-aided system is a solution to this problem. This paper presents an automated kidney segmentation technique based on active contour and deep learning. MATERIALS AND METHODS In this work, 210 CTs from the KiTS 19 repository were used. The used dataset was divided into a train set (168 CTs), test set (21 CTs), and validation set (21 CTs). The suggested technique has broadly four phases: (1) extraction of kidney regions using active contours, (2) preprocessing, (3) kidney segmentation using 3D U-Net, and (4) reconstruction of the segmented CT images. RESULTS The proposed segmentation method has received the Dice score of 97.62%, Jaccard index of 95.74%, average sensitivity of 98.28%, specificity of 99.95%, and accuracy of 99.93% over the validation dataset. CONCLUSION The proposed method can efficiently solve the problem of tumorous kidney segmentation in CT images by using active contour and deep learning. The active contour was used to select kidney regions and 3D-UNet was used for precisely segmenting the tumorous kidney.
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Affiliation(s)
- Mohit Pandey
- School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra-182320, Jammu & Kashmir, India
| | - Abhishek Gupta
- School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra-182320, Jammu & Kashmir, India.
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14
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Valente S, Morais P, Torres HR, Oliveira B, Gomes-Fonseca J, Buschle LR, Fritz A, Correia-Pinto J, Lima E, Vilaca JL. A deep learning method for kidney segmentation in 2D ultrasound images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3911-3914. [PMID: 36086291 DOI: 10.1109/embc48229.2022.9871748] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Ultrasound (US) is a medical imaging modality widely used for diagnosis, monitoring, and guidance of surgical procedures. However, the accurate interpretation of US images is a challenging task. Recently, portable 2D US devices enhanced with Artificial intelligence (AI) methods to identify, in real-time, specific organs are widely spreading worldwide. Nevertheless, the number of available methods that effectively work in such devices is still limited. In this work, we evaluate the performance of the U-NET architecture to segment the kidney in 2D US images. To accomplish this task, we studied the possibility of using multiple sliced images extracted from 3D US volumes to achieve a large, variable, and multi-view dataset of 2D images. The proposed methodology was tested with a dataset of 66 3D US volumes, divided in 51 for training, 5 for validation, and 10 for testing. From the volumes, 3792 2D sliced images were extracted. Two experiments were conducted, namely: (i) using the entire database (WWKD); and (ii) using images where the kidney area is > 500 mm2 (500KD). As a proof-of-concept, the potential of our strategy was tested in real 2D images (acquired with 2D probes). An average error of 2.88 ± 2.63 mm in the testing dataset was registered. Moreover, satisfactory results were obtained in our initial proof-of-concept using pure 2D images. In short, the proposed method proved, in this preliminary study, its potential interest for clinical practice. Further studies are required to evaluate the real performance of the proposed methodology. Clinical Relevance- In this work a deep learning methodology to segment the kidney in 2D US images is presented. It may be a relevant feature to be included in the recent portable US ecosystems easing the interpretation of image and consequently the clinical analysis.
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15
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The Spatial Distribution of Renal Fibrosis Investigated by Micro-probe Terahertz Spectroscopy System. Diagnostics (Basel) 2022; 12:diagnostics12071602. [PMID: 35885509 PMCID: PMC9316920 DOI: 10.3390/diagnostics12071602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/23/2022] [Accepted: 06/28/2022] [Indexed: 12/02/2022] Open
Abstract
Renal fibrosis, which is characterized as progressive extracellular matrix accumulation, is a common feature of different stages of chronic kidney disease, and the degree of fibrosis is strongly associated with renal function. In clinical practice, precise understanding of the space distribution of fibrosis is extremely important for the diagnosis and prognosis of renal disease. Rapid advances in terahertz (THz) technology have been made, and this technology has a broad application in bio-detection, as it can interact and measure the collective vibrations and rotations of molecular groups. It is well known that hydroxyproline (HYP) is the key component of collagen, which is synthesized by fibroblasts to maintain the extracellular matrix, and HYP content detection in tissue homogenate can be achieved by classical biochemistry method. In this study, a THz microprobe system was employed to conduct THz microspatial scanning with a resolution of 20 µm. Both the content and distribution of HYP were directly characterized by the THz absorption spectrum. The absorption intensity in the THz spectrum was used to determine HYP density in renal tissues; therefore, the fibrosis change in the kidneys can be determined using THz scanning at micrometer resolution, which provides more possibilities for precise diagnosis of renal fibrosis.
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16
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Alex DM, Chandy DA, Christinal AH, Singh A, Pushkaran M. A Hybrid Random Forest Classifier for Chronic Kidney Disease Prediction from 2D Ultrasound Kidney Images. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422560109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Chronic kidney disease (CKD) is one of the causes of mortality in almost all countries across the globe and the notable thing is its asymptomatic nature in the early stages. This disease is characterized by the gradual loss of kidney function in an individual. Frequently chronic kidney disease is diagnosed based on the Estimated Glomerular Filtration Rate (eGFR) determined from blood and urine tests. In order to reduce the risk factors arising due to chronic kidney disease, it is essential to be diagnosed in the earlier stages itself. This work proposes an automated chronic kidney disease detection based on the textural features of the kidney using a hybrid random forest classifier from 2D ultrasound kidney images. The proposed classifier is compared with the other competing machine learning classifiers through experimenting on a dataset of 150 images and gives a better accuracy of [Formula: see text] with [Formula: see text] of recall and precision, thus proving it to be promising in detecting CKD noninvasively in the early stages.
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Affiliation(s)
- Deepthy Mary Alex
- Department of ECE, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore - 641114, Tamil Nadu, India
| | - D. Abraham Chandy
- Department of ECE, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore - 641114, Tamil Nadu, India
| | - A. Hepzibah Christinal
- Department of Mathematics, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore - 641114, Tamil Nadu, India
| | - Arvinder Singh
- Department of Radiology, Sri Guru Ram Das Institute of Medical Sciences and Research, Sri Amritsar - 143501, Punjab, India
| | - M. Pushkaran
- Radiology Division, Kovai Diagnostic Centre, Coimbatore-641012, Tamil Nadu, India
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Chen G, Yin J, Dai Y, Zhang J, Yin X, Cui L. A novel convolutional neural network for kidney ultrasound images segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106712. [PMID: 35248816 DOI: 10.1016/j.cmpb.2022.106712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/27/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Ultrasound imaging has been widely used in the screening of kidney diseases. The localization and segmentation of the kidneys in ultrasound images are helpful for the clinical diagnosis of diseases. However, it is a challenging task to segment the kidney accurately from ultrasound images due to the interference of various factors. METHODS In this paper, a novel multi-scale and deep-supervised CNN architecture is proposed to segment the kidney. The architecture consists of an encoder, a pyramid pooling module and a decoder. In the encoder, we design a multi-scale input pyramid with parallel branches to capture features at different scales. In the decoder, a multi-output supervision module is developed. The introduction of the multi-output supervision module enables the network to learn to predict more precise segmentation results scale-by-scale. In addition, we construct a kidney ultrasound dataset, which contains of 400 images and 400 labels. RESULTS To highlight effectiveness of the proposed approach, we use six quantitative indicators to compare with several state-of-the-art methods on the same kidney ultrasound dataset. The results of our method on the six indicators of accuracy, dice, jaccard, precision, recall and ASSD are 98.86%, 95.86%, 92.18%, 96.38%, 95.47% and 0.3510, respectively. CONCLUSIONS The analysis of evaluation indicators and segmentation results shows that our method achieves the best performance in kidney ultrasound image segmentation.
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Affiliation(s)
- Gongping Chen
- The Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
| | - Jingjing Yin
- The Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Yu Dai
- The Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
| | - Jianxun Zhang
- The Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Xiaotao Yin
- Department of Urology, Fourth Medical Center of Chinese PLA General Hospital, Beijing 10048, China
| | - Liang Cui
- Department of Urology, Civil Aviation General Hospital, Beijing 100123, China
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18
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Song Y, Zheng J, Lei L, Ni Z, Zhao B, Hu Y. CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data. ULTRASONICS 2022; 122:106706. [PMID: 35149255 DOI: 10.1016/j.ultras.2022.106706] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/16/2022] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
Accurate segmentation of kidney in ultrasound images is a vital procedure in clinical diagnosis and interventional operation. In recent years, deep learning technology has demonstrated promising prospects in medical image analysis. However, due to the inherent problems of ultrasound images, data with annotations are scarce and arduous to acquire, hampering the application of data-hungry deep learning methods. In this paper, we propose cross-modal transfer learning from computerized tomography (CT) to ultrasound (US) by leveraging annotated data in the CT modality. In particular, we adopt cycle generative adversarial network (CycleGAN) to synthesize US images from CT data and construct a transition dataset to mitigate the immense domain discrepancy between US and CT. Mainstream convolutional neural networks such as U-Net, U-Res, PSPNet, and DeepLab v3+ are pretrained on the transition dataset and then transferred to real US images. We first trained CNN models on a data set composed of 50 ultrasound images and validated them on a validation set composed of 30 ultrasound images. In addition, we selected 82 ultrasound images from another hospital to construct a cross-site data set to verify the generalization performance of the models. The experimental results show that with our proposed transfer learning strategy, the segmentation accuracy in dice similarity coefficient (DSC) reaches 0.853 for U-Net, 0.850 for U-Res, 0.826 for PSPNet and 0.827 for DeepLab v3+ on the cross-site test set. Compared with training from scratch, the accuracy improvement was 0.127, 0.097, 0.105 and 0.036 respectively. Our transfer learning strategy effectively improves the accuracy and generalization ability of ultrasound image segmentation model with limited training data.
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Affiliation(s)
- Yuxin Song
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China and University of Chinese Academy of Sciences, Beijing 100039, China.
| | - Jing Zheng
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical College of Jinan University, Shenzhen People's Hospital, Shenzhen, 518020, China.
| | - Long Lei
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
| | - Zhipeng Ni
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical College of Jinan University, Shenzhen People's Hospital, Shenzhen, 518020, China.
| | - Baoliang Zhao
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Pazhou Lab, Guangzhou 510320, China.
| | - Ying Hu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Pazhou Lab, Guangzhou 510320, China.
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19
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Jagtap JM, Gregory AV, Homes HL, Wright DE, Edwards ME, Akkus Z, Erickson BJ, Kline TL. Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements. Abdom Radiol (NY) 2022; 47:2408-2419. [PMID: 35476147 PMCID: PMC9226108 DOI: 10.1007/s00261-022-03521-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 11/01/2022]
Abstract
PURPOSE Total kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert annotators. We investigated a deep learning-based approach for automated segmentation of TKV from 3D US in ADPKD patients. METHOD We used axially acquired 3D US-kidney images in 22 ADPKD patients where each patient and each kidney were scanned three times, resulting in 132 scans that were manually segmented. We trained a convolutional neural network to segment the whole kidney and measure TKV. All patients were subsequently imaged with MRI for measurement comparison. RESULTS Our method automatically segmented polycystic kidneys in 3D US images obtaining an average Dice coefficient of 0.80 on the test dataset. The kidney volume measurement compared with linear regression coefficient and bias from human tracing were R2 = 0.81, and - 4.42%, and between AI and reference standard were R2 = 0.93, and - 4.12%, respectively. MRI and US measured kidney volumes had R2 = 0.84 and a bias of 7.47%. CONCLUSION This is the first study applying deep learning to 3D US in ADPKD. Our method shows promising performance for auto-segmentation of kidneys using 3D US to measure TKV, close to human tracing and MRI measurement. This imaging and analysis method may be useful in a number of settings, including pediatric imaging, clinical studies, and longitudinal tracking of patient disease progression.
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20
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Torres HR, Morais P, Oliveira B, Birdir C, Rüdiger M, Fonseca JC, Vilaça JL. A review of image processing methods for fetal head and brain analysis in ultrasound images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106629. [PMID: 35065326 DOI: 10.1016/j.cmpb.2022.106629] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 12/20/2021] [Accepted: 01/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods. METHODS In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement. RESULTS For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications. CONCLUSIONS A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection.
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Affiliation(s)
- Helena R Torres
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; 2Ai - School of Technology, IPCA, Barcelos, Portugal.
| | - Pedro Morais
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Bruno Oliveira
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Cahit Birdir
- Department of Gynecology and Obstetrics, University Hospital Carl Gustav Carus, TU Dresden, Germany; Saxony Center for Feto-Neonatal Health, TU Dresden, Germany
| | - Mario Rüdiger
- Department for Neonatology and Pediatric Intensive Care, University Hospital Carl Gustav Carus, TU Dresden, Germany
| | - Jaime C Fonseca
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - João L Vilaça
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
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21
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Abdelrahman A, Viriri S. Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art. J Imaging 2022; 8:jimaging8030055. [PMID: 35324610 PMCID: PMC8954467 DOI: 10.3390/jimaging8030055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/26/2022] [Accepted: 02/10/2022] [Indexed: 01/27/2023] Open
Abstract
Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic procedures for early detection and diagnosis are crucial. Some difficulties with manual segmentation have necessitated the use of deep learning models to assist clinicians in effectively recognizing and segmenting tumors. Deep learning (DL), particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images. Simultaneously, researchers in the field of medical image segmentation employ DL approaches to solve problems such as tumor segmentation, cell segmentation, and organ segmentation. Segmentation of tumors semantically is critical in radiation and therapeutic practice. This article discusses current advances in kidney tumor segmentation systems based on DL. We discuss the various types of medical images and segmentation techniques and the assessment criteria for segmentation outcomes in kidney tumor segmentation, highlighting their building blocks and various strategies.
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22
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Kahn C, Leichter I, Lederman R, Sosna J, Duvdevani M, Yeshua T. Quantitative assessment of renal obstruction in multi-phase CTU using automatic 3D segmentation of the renal parenchyma and renal pelvis: A proof of concept. Eur J Radiol Open 2022; 9:100458. [DOI: 10.1016/j.ejro.2022.100458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 11/27/2022] Open
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23
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Chen G, Dai Y, Zhang J, Yin X, Cui L. MBANet: Multi-branch aware network for kidney ultrasound images segmentation. Comput Biol Med 2021; 141:105140. [PMID: 34922172 DOI: 10.1016/j.compbiomed.2021.105140] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 12/11/2021] [Accepted: 12/11/2021] [Indexed: 12/18/2022]
Abstract
Due to the influence of kidney morphology, heterogeneous structure and image quality, segmenting kidney in ultrasound images is challenging. To alleviate this challenge, we proposed a novel deep neural network architecture, namely Multi-branch Aware Network (MBANet), to segment kidney accurately and robustly. MBANet mainly consists of multi-scale feature pyramid (MSFP), multi-branch encoders (MBE) and master decoder. The design of MSFP can make the network more accessible to different kinds of class details at different scales. The information exchange between MBE can reduce the loss of feature information and improve the segmentation accuracy of the network. In addition, we designed a multi-scale fusion block (MFBlock) in the MBE to further extract and fuse more refined multi-scale image information. In order to further improve the robustness of MBANet, this paper also designed a step-by-step training mechanism. We validated the proposed approach and compared to several state-of-the-art approaches on the same kidney ultrasound datasets using six quantitative metrics. The results of our method on the six indicators of pixel accuracy (PA), intersection over union (IoU), precision, recall, specificity and F1-score (F1) are 98.83%, 92.38%, 97.10%, 95.03%, 99.46% and 0.9601, respectively. Compared with the comparison method, the average values on the six indicators are improved by about 2%. The evaluation results and segmentation results demonstrate that the proposed approach achieves the best overall performance on kidney ultrasound images segmentation.
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Affiliation(s)
- Gongping Chen
- The Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, College of Artificial Intelligence, Nankai University, Tianjin, 300350, China.
| | - Yu Dai
- The Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, College of Artificial Intelligence, Nankai University, Tianjin, 300350, China.
| | - Jianxun Zhang
- The Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, College of Artificial Intelligence, Nankai University, Tianjin, 300350, China
| | - Xiaotao Yin
- Department of Urology, Civil Aviation General Hospital, Beijing, 100123, China
| | - Liang Cui
- Department of Urology, Fourth Medical Center of Chinese PLA General Hospital, Beijing, 10048, China
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24
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Tang P, Zhao YQ, Liao M. Automatic multi-organ segmentation from abdominal CT volumes with LLE-based graph partitioning and 3D Chan-Vese model. Comput Biol Med 2021; 139:105030. [PMID: 34800809 DOI: 10.1016/j.compbiomed.2021.105030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/09/2021] [Accepted: 11/10/2021] [Indexed: 11/16/2022]
Abstract
This paper presents a fully automatic method for multi-organ segmentation from 3D abdominal CT volumes. Firstly, spines and ribs are removed by exponential transformation and binarization to reduce the disturbance to subsequent segmentation. Then, a Local Linear Embedding (LLE)-based graph partitioning approach is employed to perform initial segmentation for liver, spleen, and bilateral kidneys simultaneously, and a novel segmentation refinement scheme is applied composed of hybrid intensity model, 3D Chan-Vese model, and histogram equalization-based organ separation algorithm. Finally, a pseudo-3D bottleneck detection algorithm is introduced for boundary correction. The proposed method does not require heavy training or registration process and is capable of dealing with shape and location variations as well as the weak boundaries of target organs. Experiments on XHCSU20 database show the proposed method is competitive with state-of-the-art methods with Dice similarity coefficients of 95.9%, 95.1%, 94.7%, and 94.5%, Jaccard indices of 92.2%, 90.8%, 90.0%, and 89.5%, and average symmetric surface distances of 1.1 mm, 1.0 mm, 0.9 mm and 1.1 mm, for liver, spleen, left and right kidneys, respectively, and the average running time is around 6 min for a CT volume. The accuracy, precision, recall, and specificity also maintain high values for each of the four organs. Moreover, experiments on SLIVER07 dataset prove its high efficiency and accuracy on liver-only segmentation.
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Affiliation(s)
- Ping Tang
- School of Automation, Central South University, Changsha, 410083, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yu-Qian Zhao
- School of Automation, Central South University, Changsha, 410083, China; Hunan Xiangjiang Artificial Intelligence Academy, Changsha, 410083, China; Hunan Engineering Research Center of High Strength Fastener Intelligent Manufacturing, Changde, 415701, China.
| | - Miao Liao
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
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Kim DW, Ahn HG, Kim J, Yoon CS, Kim JH, Yang S. Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children. SENSORS 2021; 21:s21206846. [PMID: 34696057 PMCID: PMC8539895 DOI: 10.3390/s21206846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/04/2021] [Accepted: 10/12/2021] [Indexed: 11/23/2022]
Abstract
In this study, we aimed to develop a new automated method for kidney volume measurement in children using ultrasonography (US) with image pre-processing and hybrid learning and to formulate an equation to calculate the expected kidney volume. The volumes of 282 kidneys (141 subjects, <19 years old) with normal function and structure were measured using US. The volumes of 58 kidneys in 29 subjects who underwent US and computed tomography (CT) were determined by image segmentation and compared to those calculated by the conventional ellipsoidal method and CT using intraclass correlation coefficients (ICCs). An expected kidney volume equation was developed using multivariate regression analysis. Manual image segmentation was automated using hybrid learning to calculate the kidney volume. The ICCs for volume determined by image segmentation and ellipsoidal method were significantly different, while that for volume calculated by hybrid learning was significantly higher than that for ellipsoidal method. Volume determined by image segmentation was significantly correlated with weight, body surface area, and height. Expected kidney volume was calculated as (2.22 × weight (kg) + 0.252 × height (cm) + 5.138). This method will be valuable in establishing an age-matched normal kidney growth chart through the accumulation and analysis of large-scale data.
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Affiliation(s)
- Dong-Wook Kim
- Department of Biomedical Engineering, Yonsei University, Wonju 26494, Korea; (D.-W.K.); (H.-G.A.); (J.K.)
| | - Hong-Gi Ahn
- Department of Biomedical Engineering, Yonsei University, Wonju 26494, Korea; (D.-W.K.); (H.-G.A.); (J.K.)
| | - Jeeyoung Kim
- Department of Biomedical Engineering, Yonsei University, Wonju 26494, Korea; (D.-W.K.); (H.-G.A.); (J.K.)
| | - Choon-Sik Yoon
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea;
| | - Ji-Hong Kim
- Department of Pediatrics, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea
- Correspondence: (J.-H.K.); (S.Y.); Tel.: +82-2-2019-3352 (J.-H.K.); +82-33-760-2459 (S.Y.)
| | - Sejung Yang
- Department of Biomedical Engineering, Yonsei University, Wonju 26494, Korea; (D.-W.K.); (H.-G.A.); (J.K.)
- Correspondence: (J.-H.K.); (S.Y.); Tel.: +82-2-2019-3352 (J.-H.K.); +82-33-760-2459 (S.Y.)
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26
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Pandey M, Gupta A. A systematic review of the automatic kidney segmentation methods in abdominal images. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.10.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Fang L, Zhang L, Yao Y. Integrating a learned probabilistic model with energy functional for ultrasound image segmentation. Med Biol Eng Comput 2021; 59:1917-1931. [PMID: 34383220 DOI: 10.1007/s11517-021-02411-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 07/03/2021] [Indexed: 11/26/2022]
Abstract
The segmentation of ultrasound (US) images is steadily growing in popularity, owing to the necessity of computer-aided diagnosis (CAD) systems and the advantages that this technique shows, such as safety and efficiency. The objective of this work is to separate the lesion from its background in US images. However, most US images contain poor quality, which is affected by the noise, ambiguous boundary, and heterogeneity. Moreover, the lesion region may be not salient amid the other normal tissues, which makes its segmentation a challenging problem. In this paper, an US image segmentation algorithm that combines the learned probabilistic model with energy functionals is proposed. Firstly, a learned probabilistic model based on the generalized linear model (GLM) reduces the false positives and increases the likelihood energy term of the lesion region. It yields a new probability projection that attracts the energy functional toward the desired region of interest. Then, boundary indicator and probability statistical-based energy functional are used to provide a reliable boundary for the lesion. Integrating probabilistic information into the energy functional framework can effectively overcome the impact of poor quality and further improve the accuracy of segmentation. To verify the performance of the proposed algorithm, 40 images are randomly selected in three databases for evaluation. The values of DICE coefficient, the Jaccard distance, root-mean-square error, and mean absolute error are 0.96, 0.91, 0.059, and 0.042, respectively. Besides, the initialization of the segmentation algorithm and the influence of noise are also analyzed. The experiment shows a significant improvement in performance. A. Description of the proposed paper. B. The main steps involved in the proposed method.
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Affiliation(s)
- Lingling Fang
- Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China.
- Nanchang Institute of Technology, City, Nanchang, Jiangxi Province, China.
| | - Lirong Zhang
- Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China
| | - Yibo Yao
- Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China
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Alex DM, Chandy DA. Exploration of a Framework for the Identification of Chronic Kidney Disease Based on 2D Ultrasound Images: A Survey. Curr Med Imaging 2021; 17:464-478. [PMID: 32964826 DOI: 10.2174/1573405616666200923162600] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 07/20/2020] [Accepted: 08/05/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND Chronic kidney disease (CKD) is a fatal disease that ultimately results in kidney failure. The primary threat is the aetiology of CKD. Over the years, researchers have proposed various techniques and methods to detect and diagnose the disease. The conventional method of detecting CKD is the determination of the estimated glomerular filtration rate by measuring creatinine levels in blood or urine. Conventional methods for the detection and classification of CKD are tedious; therefore, several researchers have suggested various alternative methods. Recently, the research community has shown keen interest in developing methods for the early detection of this disease using imaging modalities such as ultrasound, magnetic resonance imaging, and computed tomography. DISCUSSION The study aimed to conduct a systematic review of various existing techniques for the detection and classification of different stages of CKD using 2D ultrasound imaging of the kidney. The review was confined to 2D ultrasound images alone, considering the feasibility of implementation even in underdeveloped countries because 2D ultrasound scans are more cost effective than other modalities. The techniques and experimentation in each work were thoroughly studied and discussed in this review. CONCLUSION This review displayed the cutting-age research, challenges, and possibilities of further research and development in the detection and classification of CKD.
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Affiliation(s)
- Deepthy Mary Alex
- Department of Electronics and Communication Engineering, Karunya University Institute of Technology and Sciences, Coimbatore, India
| | - D Abraham Chandy
- Department of Electronics and Communication Engineering, Karunya University Institute of Technology and Sciences, Coimbatore, India
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Torres HR, Queiros S, Morais P, Oliveira B, Gomes-Fonseca J, Mota P, Lima E, D'Hooge J, Fonseca JC, Vilaca JL. Kidney Segmentation in 3-D Ultrasound Images Using a Fast Phase-Based Approach. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2021; 68:1521-1531. [PMID: 33211657 DOI: 10.1109/tuffc.2020.3039334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Renal ultrasound (US) imaging is the primary imaging modality for the assessment of the kidney's condition and is essential for diagnosis, treatment and surgical intervention planning, and follow-up. In this regard, kidney delineation in 3-D US images represents a relevant and challenging task in clinical practice. In this article, a novel framework is proposed to accurately segment the kidney in 3-D US images. The proposed framework can be divided into two stages: 1) initialization of the segmentation method and 2) kidney segmentation. Within the initialization stage, a phase-based feature detection method is used to detect edge points at kidney boundaries, from which the segmentation is automatically initialized. In the segmentation stage, the B-spline explicit active surface framework is adapted to obtain the final kidney contour. Here, a novel hybrid energy functional that combines localized region- and edge-based terms is used during segmentation. For the edge term, a fast-signed phase-based detection approach is applied. The proposed framework was validated in two distinct data sets: 1) 15 3-D challenging poor-quality US images used for experimental development, parameters assessment, and evaluation and 2) 42 3-D US images (both healthy and pathologic kidneys) used to unbiasedly assess its accuracy. Overall, the proposed method achieved a Dice overlap around 81% and an average point-to-surface error of ~2.8 mm. These results demonstrate the potential of the proposed method for clinical usage.
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Badic B, Tixier F, Cheze Le Rest C, Hatt M, Visvikis D. Radiogenomics in Colorectal Cancer. Cancers (Basel) 2021; 13:cancers13050973. [PMID: 33652647 PMCID: PMC7956421 DOI: 10.3390/cancers13050973] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/07/2021] [Accepted: 02/20/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Colorectal carcinoma is characterized by intratumoral heterogeneity that can be assessed by radiogenomics. Radiomics, high-throughput quantitative data extracted from medical imaging, combined with molecular analysis, through genomic and transcriptomic data, is expected to lead to significant advances in personalized medicine. However, a radiogenomics approach in colorectal cancer is still in its early stages and many problems remain to be solved. Here we review the progress and challenges in this field at its current stage, as well as future developments. Abstract The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features—radiomics and genetic profile modifications—genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.
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Affiliation(s)
- Bogdan Badic
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
- Correspondence: ; Tel.: +33-298-347-215
| | - Florent Tixier
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
| | - Catherine Cheze Le Rest
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
- Department of Nuclear Medicine, University Hospital of Poitiers, 86021 Poitiers, France
| | - Mathieu Hatt
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
| | - Dimitris Visvikis
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
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31
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Alnazer I, Bourdon P, Urruty T, Falou O, Khalil M, Shahin A, Fernandez-Maloigne C. Recent advances in medical image processing for the evaluation of chronic kidney disease. Med Image Anal 2021; 69:101960. [PMID: 33517241 DOI: 10.1016/j.media.2021.101960] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 11/18/2020] [Accepted: 12/31/2020] [Indexed: 12/31/2022]
Abstract
Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.
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Affiliation(s)
- Israa Alnazer
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France; AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon.
| | - Pascal Bourdon
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
| | - Thierry Urruty
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
| | - Omar Falou
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon; American University of Culture and Education, Koura, Lebanon; Lebanese University, Faculty of Science, Tripoli, Lebanon
| | - Mohamad Khalil
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Ahmad Shahin
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Christine Fernandez-Maloigne
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
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32
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Glass-cutting medical images via a mechanical image segmentation method based on crack propagation. Nat Commun 2020; 11:5669. [PMID: 33168802 PMCID: PMC7652839 DOI: 10.1038/s41467-020-19392-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 10/07/2020] [Indexed: 11/23/2022] Open
Abstract
Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential. Automatic segmentation of complex medical images is challenging. Here, the authors present a crack propagation method based on the principles of fracture mechanics: extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge.
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33
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Siriapisith T, Kusakunniran W, Haddawy P. Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation. Comput Biol Med 2020; 126:103997. [PMID: 32987203 DOI: 10.1016/j.compbiomed.2020.103997] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/30/2020] [Accepted: 08/30/2020] [Indexed: 11/17/2022]
Abstract
Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.
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Affiliation(s)
- Thanongchai Siriapisith
- Department Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.
| | - Worapan Kusakunniran
- Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, 73170, Thailand
| | - Peter Haddawy
- Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, 73170, Thailand; Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
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34
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Fatemeh Z, Nicola S, Satheesh K, Eranga U. Ensemble U-net-based method for fully automated detection and segmentation of renal masses on computed tomography images. Med Phys 2020; 47:4032-4044. [PMID: 32329074 DOI: 10.1002/mp.14193] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 04/06/2020] [Accepted: 04/15/2020] [Indexed: 01/07/2023] Open
Abstract
PURPOSE Detection and accurate localization of renal masses (RM) are important steps toward future potential classification of benign vs malignant RM. A fully automated algorithm for detection and localization of RM may eliminate the observer variability in the clinical workflow. METHOD In this paper, we describe a fully automated methodology for accurate detection and segmentation of RM from contrast-enhanced computed tomography (CECT) images. We first determine the boundaries of the kidneys on the CECT images utilizing a convolutional neural network-based method to be used as a region of interest to search for RM. We then employ a homogenous U-Net-based ensemble learning model to identify and delineate RM. We used an institutional dataset comprised of CECT images in 315 patients to train and evaluate the proposed method. We compared results of our method to those of three-dimensional (3D) U-Net for RM localization and further evaluated our algorithm using the kidney tumor segmentation (KiTS19) challenge dataset. RESULTS The developed algorithm reported a Dice similarity coefficient (DSC) of 95.79% ± 5.16% and 96.25 ± 3.37 (mean ± standard deviation) for segmentation accuracy of kidney boundary from 125 and 60 test images from institutional and KiTS19 datasets, respectively. Using our method, RM were detected in 125 and 52 test cases, which corresponds to 100% and 86.67% sensitivity at patient level in institutional and KiTS19 test images. Our ensemble method for RM localization yielded a mean DSC of 88.65% ± 7.31% and 87.91% ± 6.82% on the institutional and KiTS19 test datasets, respectively. The mean DSC for RM delineation from CECT institutional test images using 3D U-Net was 85.95% ± 1.46%. CONCLUSION We describe a method for automated localization of RM using CECT images. Our results are important in terms of clinical perspective as fully automated detection of RM is a fundamental step for further diagnosis of cystic vs solid RM and eventually benign vs malignant solid RM, that has not been reported previously.
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Affiliation(s)
- Zabihollahy Fatemeh
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - Schieda Nicola
- Department of Radiology, University of Ottawa, Ottawa, ON, Canada
| | - Krishna Satheesh
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Ukwatta Eranga
- School of Engineering, University of Guelph, Guelph, ON, Canada
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Yin S, Peng Q, Li H, Zhang Z, You X, Fischer K, Furth SL, Tasian GE, Fan Y. Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks. Med Image Anal 2020; 60:101602. [PMID: 31760193 PMCID: PMC6980346 DOI: 10.1016/j.media.2019.101602] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 07/22/2019] [Accepted: 11/07/2019] [Indexed: 12/28/2022]
Abstract
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we propose subsequent boundary distance regression and pixel classification networks to segment the kidneys automatically. Particularly, we first use deep neural networks pre-trained for classification of natural images to extract high-level image features from US images. These features are used as input to learn kidney boundary distance maps using a boundary distance regression network and the predicted boundary distance maps are classified as kidney pixels or non-kidney pixels using a pixelwise classification network in an end-to-end learning fashion. We also adopted a data-augmentation method based on kidney shape registration to generate enriched training data from a small number of US images with manually segmented kidney labels. Experimental results have demonstrated that our method could automatically segment the kidney with promising performance, significantly better than deep learning-based pixel classification networks.
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Affiliation(s)
- Shi Yin
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Qinmu Peng
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China; Shenzhen Huazhong University of Science and Technology Research Institute, China.
| | - Hongming Li
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Zhengqiang Zhang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Xinge You
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China; Shenzhen Huazhong University of Science and Technology Research Institute, China
| | - Katherine Fischer
- Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States; Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Susan L Furth
- Department of Pediatrics, Division of Pediatric Nephrology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Gregory E Tasian
- Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States; Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States; Department of Biostatistics, Epidemiology, and Informatics, The University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.
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Lenchik L, Heacock L, Weaver AA, Boutin RD, Cook TS, Itri J, Filippi CG, Gullapalli RP, Lee J, Zagurovskaya M, Retson T, Godwin K, Nicholson J, Narayana PA. Automated Segmentation of Tissues Using CT and MRI: A Systematic Review. Acad Radiol 2019; 26:1695-1706. [PMID: 31405724 PMCID: PMC6878163 DOI: 10.1016/j.acra.2019.07.006] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/17/2019] [Accepted: 07/17/2019] [Indexed: 01/10/2023]
Abstract
RATIONALE AND OBJECTIVES The automated segmentation of organs and tissues throughout the body using computed tomography and magnetic resonance imaging has been rapidly increasing. Research into many medical conditions has benefited greatly from these approaches by allowing the development of more rapid and reproducible quantitative imaging markers. These markers have been used to help diagnose disease, determine prognosis, select patients for therapy, and follow responses to therapy. Because some of these tools are now transitioning from research environments to clinical practice, it is important for radiologists to become familiar with various methods used for automated segmentation. MATERIALS AND METHODS The Radiology Research Alliance of the Association of University Radiologists convened an Automated Segmentation Task Force to conduct a systematic review of the peer-reviewed literature on this topic. RESULTS The systematic review presented here includes 408 studies and discusses various approaches to automated segmentation using computed tomography and magnetic resonance imaging for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications. CONCLUSION These insights should help prepare radiologists to better evaluate automated segmentation tools and apply them not only to research, but eventually to clinical practice.
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Affiliation(s)
- Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157.
| | - Laura Heacock
- Department of Radiology, NYU Langone, New York, New York
| | - Ashley A Weaver
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Robert D Boutin
- Department of Radiology, University of California Davis School of Medicine, Sacramento, California
| | - Tessa S Cook
- Department of Radiology, University of Pennsylvania, Philadelphia Pennsylvania
| | - Jason Itri
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157
| | - Christopher G Filippi
- Department of Radiology, Donald and Barbara School of Medicine at Hofstra/Northwell, Lenox Hill Hospital, NY, New York
| | - Rao P Gullapalli
- Department of Radiology, University of Maryland School of Medicine, Baltimore, Maryland
| | - James Lee
- Department of Radiology, University of Kentucky, Lexington, Kentucky
| | | | - Tara Retson
- Department of Radiology, University of California San Diego, San Diego, California
| | - Kendra Godwin
- Medical Library, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joey Nicholson
- NYU Health Sciences Library, NYU School of Medicine, NYU Langone Health, New York, New York
| | - Ponnada A Narayana
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas
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37
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Gomes-Fonseca J, Queirós S, Morais P, Pinho ACM, Fonseca JC, Correia-Pinto J, Lima E, Vilaça JL. Surface-based registration between CT and US for image-guided percutaneous renal access - A feasibility study. Med Phys 2019; 46:1115-1126. [DOI: 10.1002/mp.13369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/13/2018] [Accepted: 12/19/2018] [Indexed: 12/30/2022] Open
Affiliation(s)
- João Gomes-Fonseca
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
| | - Sandro Queirós
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
- 2Ai; Polytechnic Institute of Cávado and Ave; Barcelos Portugal
| | - Pedro Morais
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
- 2Ai; Polytechnic Institute of Cávado and Ave; Barcelos Portugal
| | - António C. M. Pinho
- Department of Mechanical Engineering; School of Engineering; University of Minho; Guimarães Portugal
| | - Jaime C. Fonseca
- Algoritmi Center; School of Engineering; University of Minho; Guimarães Portugal
- Department of Industrial Electronics; School of Engineering; University of Minho; Guimarães Portugal
| | - Jorge Correia-Pinto
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
- Department of Pediatric Surgery; Hospital of Braga; Braga Portugal
| | - Estêvão Lima
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
- Deparment of Urology; Hospital of Braga; Braga Portugal
| | - João L. Vilaça
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
- 2Ai; Polytechnic Institute of Cávado and Ave; Barcelos Portugal
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Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 2018; 29:102-127. [PMID: 30553609 DOI: 10.1016/j.zemedi.2018.11.002] [Citation(s) in RCA: 700] [Impact Index Per Article: 116.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 11/19/2018] [Accepted: 11/21/2018] [Indexed: 02/06/2023]
Abstract
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.
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Affiliation(s)
- Alexander Selvikvåg Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Norway; Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Norway.
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Norway; Neuroinformatics and Image Analysis Laboratory, Department of Biomedicine, University of Bergen, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Norway.
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Batiha IM, El-Khazali R, AlSaedi A, Momani S. The General Solution of Singular Fractional-Order Linear Time-Invariant Continuous Systems with Regular Pencils. ENTROPY 2018; 20:e20060400. [PMID: 33265490 PMCID: PMC7512919 DOI: 10.3390/e20060400] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 05/19/2018] [Accepted: 05/19/2018] [Indexed: 12/02/2022]
Abstract
This paper introduces a general solution of singular fractional-order linear-time invariant (FoLTI) continuous systems using the Adomian Decomposition Method (ADM) based on the Caputo's definition of the fractional-order derivative. The complexity of their entropy lies in defining the complete solution of such systems, which depends on introducing a method of decomposing their dynamic states from their static states. The solution is formulated by converting the singular system of regular pencils into a recursive form using the sequence of transformations, which separates the dynamic variables from the algebraic variables. The main idea of this work is demonstrated via numerical examples.
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Affiliation(s)
- Iqbal M. Batiha
- Department of Mathematics, Faculty of Science, University of Jordan, Amman 11942, Jordan
| | - Reyad El-Khazali
- ECCE Department, Khalifa University, Abu-Dhabi 127788, United Arab Emirates
- Correspondence: or
| | - Ahmed AlSaedi
- Department of Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Faculty of Science, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia
| | - Shaher Momani
- Department of Mathematics, Faculty of Science, University of Jordan, Amman 11942, Jordan
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A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement. ENTROPY 2018; 20:e20050344. [PMID: 33265434 PMCID: PMC7512864 DOI: 10.3390/e20050344] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 05/02/2018] [Accepted: 05/03/2018] [Indexed: 11/17/2022]
Abstract
Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. The proposed model estimates the probability of pixels that represent edges based on the entropy of the neighboring pixels, which results in local fractional entropy. When there is a small change in the intensity values (indicating the presence of edge in the image), the local fractional entropy gives fine image details. Similarly, when no change in intensity values is present (indicating smooth texture), the LFE does not provide fine details, based on the fact that there is no edge information. Tests were conducted on a large dataset of different, poor-quality kidney images to show that the proposed model is useful and effective. A comparative study with the classical methods, coupled with the latest enhancement methods, shows that the proposed model outperforms the existing methods.
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