1
|
Albalawi E, Thakur A, Dorai DR, Bhatia Khan S, Mahesh TR, Almusharraf A, Aurangzeb K, Anwar MS. Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach. Front Comput Neurosci 2024; 18:1418546. [PMID: 38933391 PMCID: PMC11199693 DOI: 10.3389/fncom.2024.1418546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024] Open
Abstract
Background The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. Objective This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. Methods The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. Results The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. Conclusion This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.
Collapse
Affiliation(s)
- Eid Albalawi
- Department of Computer Science, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Arastu Thakur
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - D. Ramya Dorai
- Department of Information Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - Surbhi Bhatia Khan
- School of Science, Engineering and Environment, University of Salford, Manchester, United Kingdom
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - T. R. Mahesh
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - Ahlam Almusharraf
- Department of Management, College of Business Administration, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | | |
Collapse
|
2
|
Srinivasan S, Francis D, Mathivanan SK, Rajadurai H, Shivahare BD, Shah MA. A hybrid deep CNN model for brain tumor image multi-classification. BMC Med Imaging 2024; 24:21. [PMID: 38243215 PMCID: PMC10799524 DOI: 10.1186/s12880-024-01195-7] [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: 11/07/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024] Open
Abstract
The current approach to diagnosing and classifying brain tumors relies on the histological evaluation of biopsy samples, which is invasive, time-consuming, and susceptible to manual errors. These limitations underscore the pressing need for a fully automated, deep-learning-based multi-classification system for brain malignancies. This article aims to leverage a deep convolutional neural network (CNN) to enhance early detection and presents three distinct CNN models designed for different types of classification tasks. The first CNN model achieves an impressive detection accuracy of 99.53% for brain tumors. The second CNN model, with an accuracy of 93.81%, proficiently categorizes brain tumors into five distinct types: normal, glioma, meningioma, pituitary, and metastatic. Furthermore, the third CNN model demonstrates an accuracy of 98.56% in accurately classifying brain tumors into their different grades. To ensure optimal performance, a grid search optimization approach is employed to automatically fine-tune all the relevant hyperparameters of the CNN models. The utilization of large, publicly accessible clinical datasets results in robust and reliable classification outcomes. This article conducts a comprehensive comparison of the proposed models against classical models, such as AlexNet, DenseNet121, ResNet-101, VGG-19, and GoogleNet, reaffirming the superiority of the deep CNN-based approach in advancing the field of brain tumor classification and early detection.
Collapse
Affiliation(s)
- Saravanan Srinivasan
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India
| | - Divya Francis
- Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, 624622, India
| | | | - Hariharan Rajadurai
- School of Computing Science and Engineering, VIT Bhopal University, Bhopal-Indore Highway Kothrikalan, Sehore, 466114, India
| | - Basu Dev Shivahare
- School of Computing Science and Engineering, Galgotias University, Greater Noida, 203201, India
| | - Mohd Asif Shah
- Department of Economics, Kabridahar University, Po Box 250, Kebri Dehar, Ethiopia.
- Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
- Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India.
| |
Collapse
|
3
|
Pitarch C, Ungan G, Julià-Sapé M, Vellido A. Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology. Cancers (Basel) 2024; 16:300. [PMID: 38254790 PMCID: PMC10814384 DOI: 10.3390/cancers16020300] [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: 11/09/2023] [Revised: 12/28/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.
Collapse
Affiliation(s)
- Carla Pitarch
- Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech) and Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, 08034 Barcelona, Spain;
- Eurecat, Digital Health Unit, Technology Centre of Catalonia, 08005 Barcelona, Spain
| | - Gulnur Ungan
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain; (G.U.); (M.J.-S.)
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
| | - Margarida Julià-Sapé
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain; (G.U.); (M.J.-S.)
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
| | - Alfredo Vellido
- Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech) and Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, 08034 Barcelona, Spain;
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
| |
Collapse
|
4
|
Mondello A, Dal Bo M, Toffoli G, Polano M. Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges. Front Pharmacol 2024; 14:1260276. [PMID: 38264526 PMCID: PMC10803549 DOI: 10.3389/fphar.2023.1260276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/26/2023] [Indexed: 01/25/2024] Open
Abstract
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.
Collapse
Affiliation(s)
| | | | | | - Maurizio Polano
- Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Aviano, Italy
| |
Collapse
|
5
|
Raghavendra U, Gudigar A, Paul A, Goutham TS, Inamdar MA, Hegde A, Devi A, Ooi CP, Deo RC, Barua PD, Molinari F, Ciaccio EJ, Acharya UR. Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives. Comput Biol Med 2023; 163:107063. [PMID: 37329621 DOI: 10.1016/j.compbiomed.2023.107063] [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: 12/26/2022] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 06/19/2023]
Abstract
A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.
Collapse
Affiliation(s)
- U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | - Aritra Paul
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - T S Goutham
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Ajay Hegde
- Consultant Neurosurgeon Manipal Hospitals, Sarjapur Road, Bangalore, India
| | - Aruna Devi
- School of Education and Tertiary Access, University of the Sunshine Coast, Caboolture Campus, Australia
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore, 599494, Singapore
| | - Ravinesh C Deo
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW, 2010, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129, Torino, Italy
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY, 10032, USA
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, 860-8555, Japan
| |
Collapse
|
6
|
Kalaroopan D, Lasocki A. MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas. J Med Imaging Radiat Oncol 2023; 67:492-498. [PMID: 36919468 DOI: 10.1111/1754-9485.13522] [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: 11/24/2021] [Accepted: 02/16/2023] [Indexed: 03/16/2023]
Abstract
Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. This narrative review with a systematic approach identifies and synthesises the current published data on DL techniques using conventional MRI sequences for predicting isocitrate dehydrogenase (IDH) and 1p/19q-codeletion status in World Health Organisation grade 2-4 gliomas. Three databases were searched for relevant studies. In all, 13 studies met the inclusion criteria after exclusions. Key results, limitations and discrepancies between studies were synthesised. High accuracy has been reported in some studies, but the existing literature has several limitations, including generally small cohort sizes, a paucity of studies with independent testing cohorts and a lack of studies assessing IDH and 1p/19q together. While DL shows promise as a non-invasive means of predicting glioma genotype, addressing these limitations in future research will be important for facilitating clinical translation.
Collapse
Affiliation(s)
- Dinusha Kalaroopan
- Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Radiology, The University of Melbourne, Melbourne, Victoria, Australia
| |
Collapse
|
7
|
Liu L, Wang Y, Chang J, Zhang P, Xiong S, Liu H. A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images. iScience 2023; 26:106874. [PMID: 37260749 PMCID: PMC10227422 DOI: 10.1016/j.isci.2023.106874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/23/2023] [Accepted: 05/10/2023] [Indexed: 06/02/2023] Open
Abstract
The chromosome instability (CIN) is one of the hallmarks of cancer and is closely related to tumor metastasis. However, the sheer size and resolution of histopathology whole-slide images (WSIs) already challenges the capabilities of computational pathology. In this study, we propose a correlation graph attention network (MLP-GAT) that can construct graphs for classifying multi-type CINs from the WSIs of breast cancer. We construct a WSIs dataset of breast cancer from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA). Extensive experiments show that MLP-GAT far outperforms accepted state-of-the-art methods and demonstrate the advantages of the constructed graph networks for analyzing WSI data. The visualization shows the difference among the tiles in a WSI. Furthermore, the generalization performance of the proposed method was verified on the stomach cancer. This study provides guidance for studying the relationship between CIN and cancer from the perspective of image phenotype.
Collapse
Affiliation(s)
- Liangliang Liu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Ying Wang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Jing Chang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Pei Zhang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Shufeng Xiong
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Hebing Liu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| |
Collapse
|
8
|
Aouadi S, Torfeh T, Arunachalam Y, Paloor S, Riyas M, Hammoud R, Al-Hammadi N. Investigation of radiomics and deep convolutional neural networks approaches for glioma grading. Biomed Phys Eng Express 2023; 9:035020. [PMID: 36898146 DOI: 10.1088/2057-1976/acc33a] [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: 11/21/2022] [Accepted: 03/10/2023] [Indexed: 03/12/2023]
Abstract
Purpose.To determine glioma grading by applying radiomic analysis or deep convolutional neural networks (DCNN) and to benchmark both approaches on broader validation sets.Methods.Seven public datasets were considered: (1) low-grade glioma or high-grade glioma (369 patients, BraTS'20) (2) well-differentiated liposarcoma or lipoma (115, LIPO); (3) desmoid-type fibromatosis or extremity soft-tissue sarcomas (203, Desmoid); (4) primary solid liver tumors, either malignant or benign (186, LIVER); (5) gastrointestinal stromal tumors (GISTs) or intra-abdominal gastrointestinal tumors radiologically resembling GISTs (246, GIST); (6) colorectal liver metastases (77, CRLM); and (7) lung metastases of metastatic melanoma (103, Melanoma). Radiomic analysis was performed on 464 (2016) radiomic features for the BraTS'20 (others) datasets respectively. Random forests (RF), Extreme Gradient Boosting (XGBOOST) and a voting algorithm comprising both classifiers were tested. The parameters of the classifiers were optimized using a repeated nested stratified cross-validation process. The feature importance of each classifier was computed using the Gini index or permutation feature importance. DCNN was performed on 2D axial and sagittal slices encompassing the tumor. A balanced database was created, when necessary, using smart slices selection. ResNet50, Xception, EficientNetB0, and EfficientNetB3 were transferred from the ImageNet application to the tumor classification and were fine-tuned. Five-fold stratified cross-validation was performed to evaluate the models. The classification performance of the models was measured using multiple indices including area under the receiver operating characteristic curve (AUC).Results.The best radiomic approach was based on XGBOOST for all datasets; AUC was 0.934 (BraTS'20), 0.86 (LIPO), 0.73 (LIVER), (0.844) Desmoid, 0.76 (GIST), 0.664 (CRLM), and 0.577 (Melanoma) respectively. The best DCNN was based on EfficientNetB0; AUC was 0.99 (BraTS'20), 0.982 (LIPO), 0.977 (LIVER), (0.961) Desmoid, 0.926 (GIST), 0.901 (CRLM), and 0.89 (Melanoma) respectively.Conclusion.Tumor classification can be accurately determined by adapting state-of-the-art machine learning algorithms to the medical context.
Collapse
Affiliation(s)
- Souha Aouadi
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Tarraf Torfeh
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Yoganathan Arunachalam
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Satheesh Paloor
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Mohamed Riyas
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Rabih Hammoud
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Noora Al-Hammadi
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| |
Collapse
|
9
|
Dimitriadis A, Trivizakis E, Papanikolaou N, Tsiknakis M, Marias K. Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review. Insights Imaging 2022; 13:188. [PMID: 36503979 PMCID: PMC9742072 DOI: 10.1186/s13244-022-01315-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/24/2022] [Indexed: 12/14/2022] Open
Abstract
Contemporary deep learning-based decision systems are well-known for requiring high-volume datasets in order to produce generalized, reliable, and high-performing models. However, the collection of such datasets is challenging, requiring time-consuming processes involving also expert clinicians with limited time. In addition, data collection often raises ethical and legal issues and depends on costly and invasive procedures. Deep generative models such as generative adversarial networks and variational autoencoders can capture the underlying distribution of the examined data, allowing them to create new and unique instances of samples. This study aims to shed light on generative data augmentation techniques and corresponding best practices. Through in-depth investigation, we underline the limitations and potential methodology pitfalls from critical standpoint and aim to promote open science research by identifying publicly available open-source repositories and datasets.
Collapse
Affiliation(s)
- Avtantil Dimitriadis
- grid.4834.b0000 0004 0635 685XComputational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece ,grid.419879.a0000 0004 0393 8299Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
| | - Eleftherios Trivizakis
- grid.4834.b0000 0004 0635 685XComputational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece ,grid.8127.c0000 0004 0576 3437Medical School, University of Crete, 71003 Heraklion, Greece
| | - Nikolaos Papanikolaou
- grid.4834.b0000 0004 0635 685XComputational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece ,grid.421010.60000 0004 0453 9636Computational Clinical Imaging Group, Centre of the Unknown, Champalimaud Foundation, 1400-038 Lisbon, Portugal ,grid.18886.3fThe Royal Marsden NHS Foundation Trust, THe Institute of Cancer Research, London, UK
| | - Manolis Tsiknakis
- grid.4834.b0000 0004 0635 685XComputational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece ,grid.419879.a0000 0004 0393 8299Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
| | - Kostas Marias
- grid.4834.b0000 0004 0635 685XComputational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece ,grid.419879.a0000 0004 0393 8299Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
| |
Collapse
|
10
|
Saravanan S, Kumar VV, Sarveshwaran V, Indirajithu A, Elangovan D, Allayear SM. Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4380901. [PMID: 36277002 PMCID: PMC9586767 DOI: 10.1155/2022/4380901] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 09/29/2023]
Abstract
The classification of the brain tumor image is playing a vital role in the medical image domain, and it directly assists the clinicians to understand the severity and to take an appropriate solution. The magnetic resonance imaging tool is used to analyze the brain tissues and to examine the different portion of brain circumstance. We propose the convolutional neural network database learning along with neighboring network limitation (CDBLNL) technique for brain tumor image classification in medical image processing domain. The proposed system architecture is constructed with multilayer-based metadata learning, and they have integrated with CNN layer to deliver the accurate information. The metadata-based vector encoding is used, and the type of coding estimation for extra dimension is known as sparse. In order to maintain the supervised data in terms of geometric format, the atoms of neighboring limitation are built based on a well-structured k-neighbored network. The resultant of the proposed system is considerably strong and subjective for classification. The proposed system used two different datasets, such as BRATS and REMBRANDT, and the proposed brain MRI classification technique outcome is more efficient than the other existing techniques.
Collapse
Affiliation(s)
- S. Saravanan
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
| | - V. Vinoth Kumar
- Department of Computer Science and Engineering, Jain (Deemed to Be University), Bangalore, India
| | - Velliangiri Sarveshwaran
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India
| | - Alagiri Indirajithu
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014 Tamil Nadu, India
| | - D. Elangovan
- Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
| | - Shaikh Muhammad Allayear
- Department of Multimedia and Creative Technology, Daffodil International University, Daffodil Smart City, Khagan, Ashulia, Dhaka, Bangladesh
| |
Collapse
|
11
|
Tandel GS, Tiwari A, Kakde O. Performance enhancement of MRI-based brain tumor classification using suitable segmentation method and deep learning-based ensemble algorithm. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
12
|
Xie Y, Zaccagna F, Rundo L, Testa C, Agati R, Lodi R, Manners DN, Tonon C. Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives. Diagnostics (Basel) 2022; 12:diagnostics12081850. [PMID: 36010200 PMCID: PMC9406354 DOI: 10.3390/diagnostics12081850] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/20/2022] [Accepted: 07/28/2022] [Indexed: 12/21/2022] Open
Abstract
Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.
Collapse
Affiliation(s)
- Yuting Xie
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (Y.X.); (F.Z.); (R.L.); (C.T.)
| | - Fulvio Zaccagna
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (Y.X.); (F.Z.); (R.L.); (C.T.)
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy;
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy;
| | - Claudia Testa
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy;
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
| | - Raffaele Agati
- Programma Neuroradiologia con Tecniche ad elevata complessità, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy;
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (Y.X.); (F.Z.); (R.L.); (C.T.)
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy
| | - David Neil Manners
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (Y.X.); (F.Z.); (R.L.); (C.T.)
- Correspondence:
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (Y.X.); (F.Z.); (R.L.); (C.T.)
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy;
| |
Collapse
|
13
|
Eckardt JN, Bornhäuser M, Wendt K, Middeke JM. Semi-supervised learning in cancer diagnostics. Front Oncol 2022; 12:960984. [PMID: 35912249 PMCID: PMC9329803 DOI: 10.3389/fonc.2022.960984] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 06/24/2022] [Indexed: 12/01/2022] Open
Abstract
In cancer diagnostics, a considerable amount of data is acquired during routine work-up. Recently, machine learning has been used to build classifiers that are tasked with cancer detection and aid in clinical decision-making. Most of these classifiers are based on supervised learning (SL) that needs time- and cost-intensive manual labeling of samples by medical experts for model training. Semi-supervised learning (SSL), however, works with only a fraction of labeled data by including unlabeled samples for information abstraction and thus can utilize the vast discrepancy between available labeled data and overall available data in cancer diagnostics. In this review, we provide a comprehensive overview of essential functionalities and assumptions of SSL and survey key studies with regard to cancer care differentiating between image-based and non-image-based applications. We highlight current state-of-the-art models in histopathology, radiology and radiotherapy, as well as genomics. Further, we discuss potential pitfalls in SSL study design such as discrepancies in data distributions and comparison to baseline SL models, and point out future directions for SSL in oncology. We believe well-designed SSL models to strongly contribute to computer-guided diagnostics in malignant disease by overcoming current hinderances in the form of sparse labeled and abundant unlabeled data.
Collapse
Affiliation(s)
- Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
- Else Kröner Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- *Correspondence: Jan-Niklas Eckardt,
| | - Martin Bornhäuser
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
- German Consortium for Translational Cancer Research, Heidelberg, Germany
- National Center for Tumor Disease (NCT), Dresden, Germany
| | - Karsten Wendt
- Else Kröner Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Institute of Software and Multimedia Technology, Technical University Dresden, Dresden, Germany
| | - Jan Moritz Middeke
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
- Else Kröner Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| |
Collapse
|
14
|
Tran QT, Alom MZ, Orr BA. Comprehensive study of semi-supervised learning for DNA methylation-based supervised classification of central nervous system tumors. BMC Bioinformatics 2022; 23:223. [PMID: 35676649 PMCID: PMC9178802 DOI: 10.1186/s12859-022-04764-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/31/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Precision medicine for cancer treatment relies on an accurate pathological diagnosis. The number of known tumor classes has increased rapidly, and reliance on traditional methods of histopathologic classification alone has become unfeasible. To help reduce variability, validation costs, and standardize the histopathological diagnostic process, supervised machine learning models using DNA-methylation data have been developed for tumor classification. These methods require large labeled training data sets to obtain clinically acceptable classification accuracy. While there is abundant unlabeled epigenetic data across multiple databases, labeling pathology data for machine learning models is time-consuming and resource-intensive, especially for rare tumor types. Semi-supervised learning (SSL) approaches have been used to maximize the utility of labeled and unlabeled data for classification tasks and are effectively applied in genomics. SSL methods have not yet been explored with epigenetic data nor demonstrated beneficial to central nervous system (CNS) tumor classification. RESULTS This paper explores the application of semi-supervised machine learning on methylation data to improve the accuracy of supervised learning models in classifying CNS tumors. We comprehensively evaluated 11 SSL methods and developed a novel combination approach that included a self-training with editing using support vector machine (SETRED-SVM) model and an L2-penalized, multinomial logistic regression model to obtain high confidence labels from a few labeled instances. Results across eight random forest and neural net models show that the pseudo-labels derived from our SSL method can significantly increase prediction accuracy for 82 CNS tumors and 9 normal controls. CONCLUSIONS The proposed combination of semi-supervised technique and multinomial logistic regression holds the potential to leverage the abundant publicly available unlabeled methylation data effectively. Such an approach is highly beneficial in providing additional training examples, especially for scarce tumor types, to boost the prediction accuracy of supervised models.
Collapse
Affiliation(s)
- Quynh T Tran
- Department of Pathology, St. Jude Children's Research Hospital, 262 Danny Thomas Place, MS 250, Memphis, TN, 38105-3678, USA
| | - Md Zahangir Alom
- Department of Pathology, St. Jude Children's Research Hospital, 262 Danny Thomas Place, MS 250, Memphis, TN, 38105-3678, USA
| | - Brent A Orr
- Department of Pathology, St. Jude Children's Research Hospital, 262 Danny Thomas Place, MS 250, Memphis, TN, 38105-3678, USA.
| |
Collapse
|
15
|
Ali H, Biswas R, Ali F, Shah U, Alamgir A, Mousa O, Shah Z. The role of generative adversarial networks in brain MRI: a scoping review. Insights Imaging 2022; 13:98. [PMID: 35662369 PMCID: PMC9167371 DOI: 10.1186/s13244-022-01237-0] [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: 02/13/2022] [Accepted: 05/11/2022] [Indexed: 11/23/2022] Open
Abstract
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.
Collapse
Affiliation(s)
- Hazrat Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
| | - Rafiul Biswas
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Farida Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Uzair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Asma Alamgir
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Osama Mousa
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
| |
Collapse
|
16
|
Merkaj S, Bahar RC, Zeevi T, Lin M, Ikuta I, Bousabarah K, Cassinelli Petersen GI, Staib L, Payabvash S, Mongan JT, Cha S, Aboian MS. Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers (Basel) 2022; 14:cancers14112623. [PMID: 35681603 PMCID: PMC9179416 DOI: 10.3390/cancers14112623] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/21/2022] [Accepted: 05/23/2022] [Indexed: 01/27/2023] Open
Abstract
Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models-including data sources, external validation, and glioma grade classification methods -are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation.
Collapse
Affiliation(s)
- Sara Merkaj
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
- Department of Neurosurgery, University of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Ryan C. Bahar
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
- Visage Imaging, Inc., 12625 High Bluff Dr, Suite 205, San Diego, CA 92130, USA
| | - Ichiro Ikuta
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | | | - Gabriel I. Cassinelli Petersen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - Lawrence Staib
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - John T. Mongan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave., San Francisco, CA 94143, USA; (J.T.M.); (S.C.)
| | - Soonmee Cha
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave., San Francisco, CA 94143, USA; (J.T.M.); (S.C.)
| | - Mariam S. Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
- Correspondence: ; Tel.: +650-285-7577
| |
Collapse
|
17
|
Treder MS, Codrai R, Tsvetanov KA. Quality assessment of anatomical MRI images from generative adversarial networks: Human assessment and image quality metrics. J Neurosci Methods 2022; 374:109579. [PMID: 35364110 DOI: 10.1016/j.jneumeth.2022.109579] [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: 10/30/2021] [Revised: 03/01/2022] [Accepted: 03/20/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Generative Adversarial Networks (GANs) can synthesize brain images from image or noise input. So far, the gold standard for assessing the quality of the generated images has been human expert ratings. However, due to limitations of human assessment in terms of cost, scalability, and the limited sensitivity of the human eye to more subtle statistical relationships, a more automated approach towards evaluating GANs is required. NEW METHOD We investigated to what extent visual quality can be assessed using image quality metrics and we used group analysis and spatial independent components analysis to verify that the GAN reproduces multivariate statistical relationships found in real data. Reference human data was obtained by recruiting neuroimaging experts to assess real Magnetic Resonance (MR) images and images generated by a GAN. Image quality was manipulated by exporting images at different stages of GAN training. RESULTS Experts were sensitive to changes in image quality as evidenced by ratings and reaction times, and the generated images reproduced group effects (age, gender) and spatial correlations moderately well. We also surveyed a number of image quality metrics. Overall, Fréchet Inception Distance (FID), Maximum Mean Discrepancy (MMD) and Naturalness Image Quality Evaluator (NIQE) showed sensitivity to image quality and good correspondence with the human data, especially for lower-quality images (i.e., images from early stages of GAN training). However, only a Deep Quality Assessment (QA) model trained on human ratings was able to reproduce the subtle differences between higher-quality images. CONCLUSIONS We recommend a combination of group analyses, spatial correlation analyses, and both distortion metrics (FID, MMD, NIQE) and perceptual models (Deep QA) for a comprehensive evaluation and comparison of brain images produced by GANs.
Collapse
Affiliation(s)
- Matthias S Treder
- School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK.
| | - Ryan Codrai
- School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, CB2 0SZ, UK; Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
| |
Collapse
|
18
|
Ai L, Bai W, Li M. TDABNet: Three-directional attention block network for the determination of IDH status in low- and high-grade gliomas from MRI. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
19
|
Goel A, Shih G, Riyahi S, Jeph S, Dev H, Hu R, Romano D, Teichman K, Blumenfeld JD, Barash I, Chicos I, Rennert H, Prince MR. Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI. Radiol Artif Intell 2022; 4:e210205. [PMID: 35391774 PMCID: PMC8980881 DOI: 10.1148/ryai.210205] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 01/20/2022] [Accepted: 01/25/2022] [Indexed: 12/18/2022]
Abstract
This study develops, validates, and deploys deep learning for automated total kidney volume (TKV) measurement (a marker of disease severity) on T2-weighted MRI studies of autosomal dominant polycystic kidney disease (ADPKD). The model was based on the U-Net architecture with an EfficientNet encoder, developed using 213 abdominal MRI studies in 129 patients with ADPKD. Patients were randomly divided into 70% training, 15% validation, and 15% test sets for model development. Model performance was assessed using Dice similarity coefficient (DSC) and Bland-Altman analysis. External validation in 20 patients from outside institutions demonstrated a DSC of 0.98 (IQR, 0.97-0.99) and a Bland-Altman difference of 2.6% (95% CI: 1.0%, 4.1%). Prospective validation in 53 patients demonstrated a DSC of 0.97 (IQR, 0.94-0.98) and a Bland-Altman difference of 3.6% (95% CI: 2.0%, 5.2%). Last, the efficiency of model-assisted annotation was evaluated on the first 50% of prospective cases (n = 28), with a 51% mean reduction in contouring time (P < .001), from 1724 seconds (95% CI: 1373, 2075) to 723 seconds (95% CI: 555, 892). In conclusion, our deployed artificial intelligence pipeline accurately performs automated segmentation for TKV estimation of polycystic kidneys and reduces expert contouring time. Keywords: Convolutional Neural Network (CNN), Segmentation, Kidney ClinicalTrials.gov identification no.: NCT00792155 Supplemental material is available for this article. © RSNA, 2022.
Collapse
Affiliation(s)
- Akshay Goel
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - George Shih
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Sadjad Riyahi
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Sunil Jeph
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Hreedi Dev
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Rejoice Hu
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Dominick Romano
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Kurt Teichman
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Jon D. Blumenfeld
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Irina Barash
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Ines Chicos
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Hanna Rennert
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| | - Martin R. Prince
- From the Departments of Radiology (A.G., G.S., S.R., S.J., H.D.,
R.H., D.R., K.T., M.R.P.), Internal Medicine (J.D.B., I.B., I.C.), and Pathology
and Laboratory Medicine (H.R.), Weill Cornell Medicine, 525 E 68th St, New York,
NY 10021
| |
Collapse
|
20
|
Proposing Novel Data Analytics Method for Anatomical Landmark Identification from Endoscopic Video Frames. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8151177. [PMID: 35251578 PMCID: PMC8890842 DOI: 10.1155/2022/8151177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/20/2021] [Accepted: 01/07/2022] [Indexed: 11/18/2022]
Abstract
Background The anatomical landmarks contain the characteristics that are used to guide the gastroenterologists during the endoscopy. The expert can also ensure the completion of examination with the help of the anatomical landmarks. Automatic detection of anatomical landmarks in endoscopic video frames can be helpful for guiding the physicians during screening the gastrointestinal tract (GI). Method This study presents an automatic novel method for anatomical landmark detection of GI tract from endoscopic video frames based on semisupervised deep convolutional neural network (CNN) and compares the results with supervised CNN model. We consider the anatomical landmarks from Kvasir dataset that includes 500 images for each class of Z-line, pylorus, and cecum. The resolution of these images varies from 750 × 576 up to 1920 × 1072 pixels. Result Experimental results show that the supervised CNN has highly desirable performance with accuracy of 100%. Also, our proposed semisupervised CNN can compete with a slight difference similar to the CNN model. Our proposed semisupervised model trained using 1, 5, 10, and 20 percent of training data records as labeled training dataset has the average accuracy of 83%, 98%, 99%, and 99%, respectively. Conclusion The main advantage of our proposed method is achieving the high accuracy with small amount of labeled data without spending time for labeling more data. The strength of our proposed method saves the required labor, cost, and time for data labeling.
Collapse
|
21
|
Gao W, Wang W, Song D, Wang K, Lian D, Yang C, Zhu K, Zheng J, Zeng M, Rao S, Wang M. A
Multiparametric
Fusion Deep Learning Model Based on
DCE‐MRI
for Preoperative Prediction of Microvascular Invasion in Intrahepatic Cholangiocarcinoma. J Magn Reson Imaging 2022; 56:1029-1039. [PMID: 35191550 DOI: 10.1002/jmri.28126] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 02/11/2022] [Accepted: 02/11/2022] [Indexed: 12/22/2022] Open
Affiliation(s)
- Wenyu Gao
- Digital Medical Research Center School of Basic Medical Sciences, Fudan University Shanghai 200032 China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai 200032 China
| | - Wentao Wang
- Department of Radiology Cancer center, Zhongshan Hospital, Fudan University China
- Shanghai Institute of Medical Imaging Shanghai China
| | - Danjun Song
- Liver Cancer Institute, Zhongshan Hospital, Fudan University Shanghai China
- Department of Interventional Radiology Zhejiang Cancer Hospital Hangzhou Zhejiang China
| | - Kang Wang
- Digital Medical Research Center School of Basic Medical Sciences, Fudan University Shanghai 200032 China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai 200032 China
| | - Danlan Lian
- Department of Radiology Xiamen Branch, Zhongshan Hospital, Fudan University Xiamen China
| | - Chun Yang
- Department of Radiology Cancer center, Zhongshan Hospital, Fudan University China
| | - Kai Zhu
- Liver Cancer Institute, Zhongshan Hospital, Fudan University Shanghai China
| | - Jiaping Zheng
- Department of Interventional Radiology Zhejiang Cancer Hospital Hangzhou Zhejiang China
| | - Mengsu Zeng
- Department of Radiology Cancer center, Zhongshan Hospital, Fudan University China
- Shanghai Institute of Medical Imaging Shanghai China
| | - Sheng‐xiang Rao
- Department of Radiology Cancer center, Zhongshan Hospital, Fudan University China
- Shanghai Institute of Medical Imaging Shanghai China
| | - Manning Wang
- Digital Medical Research Center School of Basic Medical Sciences, Fudan University Shanghai 200032 China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai 200032 China
| |
Collapse
|
22
|
Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022; 14:cancers14030836. [PMID: 35159103 PMCID: PMC8834271 DOI: 10.3390/cancers14030836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
Collapse
Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Naureen Keric
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Florian Ringel
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Marc A. Brockmann
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
- Correspondence:
| |
Collapse
|
23
|
Al Turk L, Georgieva D, Alsawadi H, Wang S, Krause P, Alsawadi H, Alshamrani AZ, Saleh GM, Tang HL. Learning to Discover Explainable Clinical Features With Minimum Supervision. Transl Vis Sci Technol 2022; 11:11. [PMID: 35015061 PMCID: PMC8762682 DOI: 10.1167/tvst.11.1.11] [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] [Indexed: 11/24/2022] Open
Abstract
Purpose To compare supervised transfer learning to semisupervised learning for their ability to learn in-depth knowledge with limited data in the optical coherence tomography (OCT) domain. Methods Transfer learning with EfficientNet-B4 and semisupervised learning with SimCLR are used in this work. The largest public OCT dataset, consisting of 108,312 images and four categories (choroidal neovascularization, diabetic macular edema, drusen, and normal) is used. In addition, two smaller datasets are constructed, containing 31,200 images for the limited version and 4000 for the mini version of the dataset. To illustrate the effectiveness of the developed models, local interpretable model-agnostic explanations and class activation maps are used as explainability techniques. Results The proposed transfer learning approach using the EfficientNet-B4 model trained on the limited dataset achieves an accuracy of 0.976 (95% confidence interval [CI], 0.963, 0.983), sensitivity of 0.973 and specificity of 0.991. The semisupervised based solution with SimCLR using 10% labeled data and the limited dataset performs with an accuracy of 0.946 (95% CI, 0.932, 0.960), sensitivity of 0.941, and specificity of 0.983. Conclusions Semisupervised learning has a huge potential for datasets that contain both labeled and unlabeled inputs, generally, with a significantly smaller number of labeled samples. The semisupervised based solution provided with merely 10% labeled data achieves very similar performance to the supervised transfer learning that uses 100% labeled samples. Translational Relevance Semisupervised learning enables building performant models while requiring less expertise effort and time by using to good advantage the abundant amount of available unlabeled data along with the labeled samples.
Collapse
Affiliation(s)
- Lutfiah Al Turk
- Department of Statistics, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Darina Georgieva
- Department of Computer Science, University of Surrey, Guildford, Surrey, UK
| | - Hassan Alsawadi
- Department of Electrical and Computer Engineering, King Abdulaziz, University, Jeddah, Kingdom of Saudi Arabia
| | - Su Wang
- Department of Computer Science, University of Surrey, Guildford, Surrey, UK
| | - Paul Krause
- Department of Computer Science, University of Surrey, Guildford, Surrey, UK
| | - Hend Alsawadi
- Faculty of Medicine, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | | | - George M Saleh
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, UK
| | | |
Collapse
|
24
|
Nalepa J. AIM and Brain Tumors. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
25
|
Kaur J, Kaur P. Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 29:2351-2382. [PMID: 34690493 PMCID: PMC8525064 DOI: 10.1007/s11831-021-09667-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 10/01/2021] [Indexed: 06/13/2023]
Abstract
From the month of December-19, the outbreak of Coronavirus (COVID-19) triggered several deaths and overstated every aspect of individual health. COVID-19 has been designated as a pandemic by World Health Organization. The circumstances placed serious trouble on every country worldwide, particularly with health arrangements and time-consuming responses. The increase in the positive cases of COVID-19 globally spread every day. The quantity of accessible diagnosing kits is restricted because of complications in detecting the existence of the illness. Fast and correct diagnosis of COVID-19 is a timely requirement for the prevention and controlling of the pandemic through suitable isolation and medicinal treatment. The significance of the present work is to discuss the outline of the deep learning techniques with medical imaging such as outburst prediction, virus transmitted indications, detection and treatment aspects, vaccine availability with remedy research. Abundant image resources of medical imaging as X-rays, Computed Tomography Scans, Magnetic Resonance imaging, formulate deep learning high-quality methods to fight against the pandemic COVID-19. The review presents a comprehensive idea of deep learning and its related applications in healthcare received over the past decade. At the last, some issues and confrontations to control the health crisis and outbreaks have been introduced. The progress in technology has contributed to developing individual's lives. The problems faced by the radiologists during medical imaging techniques and deep learning approaches for diagnosing the COVID-19 infections have been also discussed.
Collapse
Affiliation(s)
- Jaspreet Kaur
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab India
| | - Prabhpreet Kaur
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab India
| |
Collapse
|
26
|
Sundarasekar R, Appathurai A. Efficient brain tumor detection and classification using magnetic resonance imaging. Biomed Phys Eng Express 2021; 7. [PMID: 34260415 DOI: 10.1088/2057-1976/ac0ccc] [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: 03/25/2021] [Accepted: 06/18/2021] [Indexed: 01/19/2023]
Abstract
Magnetic Resonance Imaging (MRI) inputs are most noticeable in diagnosing brain tumors via computer and manual clinical understanding. Multi-level detection and classification of the images utilizing computer-aided processing depend on labels and annotations. Though the two processes are dynamic and time-consuming, without which the precise accuracy is less assured. For augmenting the accuracy in processing un-labeled or annotation-less images, this article introduces Absolute Classification-Detection Model (AC-DM). This model uses a conventional neural network for training the morphological variations proficient of achieving label-less classification and tumor detection. The traditional neural network trains the images based on differential lattice morphology for classification and detection. In this process, training for the lattices and their corresponding gradients is validated to improve the precision of the regional analysis. This helps to retain the precision of identifying tumors. The variations are recognized for their lattice mapping in the detected boundaries of the input image. The detected boundaries help to map accurate lattices for adapting morphological transforms. Thus, the partial and complex processing in detecting tumors is restrained in the suggested model, adapting to the classification. The efficiency of the suggested model is verified utilizing accuracy, precision, sensitivity, and classification time.
Collapse
Affiliation(s)
| | - Ahilan Appathurai
- Department of ECE, Infant Jesus College of Engineering, ANNA University, Chennai, India
| |
Collapse
|
27
|
Tandel GS, Tiwari A, Kakde OG. Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification. Comput Biol Med 2021; 135:104564. [PMID: 34217980 DOI: 10.1016/j.compbiomed.2021.104564] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 06/08/2021] [Accepted: 06/08/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Although biopsy is the gold standard for tumour grading, being invasive, this procedure also proves fatal to the brain. Thus, non-invasive methods for brain tumour grading are urgently needed. Here, a magnetic resonance imaging (MRI)-based non-invasive brain tumour grading method has been proposed using deep learning (DL) and machine learning (ML) techniques. METHOD Four clinically applicable datasets were designed. The four datasets were trained and tested on five DL-based models (convolutional neural networks), AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, and five ML-based models, Support Vector Machine, K-Nearest Neighbours, Naïve Bayes, Decision Tree, and Linear Discrimination using five-fold cross-validation. A majority voting (MajVot)-based ensemble algorithm has been proposed to optimise the overall classification performance of five DL and five ML-based models. RESULTS The average accuracy improvement of four datasets using the DL-based MajVot algorithm against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 models was 2.02%, 1.11%, 1.04%, 2.67%, and 1.65%, respectively. Further, a 10.12% improvement was seen in the average accuracy of four datasets using the DL method against ML. Furthermore, the proposed DL-based MajVot algorithm was validated on synthetic face data and improved the male versus female face image classification accuracy by 2.88%, 0.71%, 1.90%, 2.24%, and 0.35% against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, respectively. CONCLUSION The proposed MajVot algorithm achieved promising results for brain tumour classification and is able to utilise the combined potential of multiple models.
Collapse
Affiliation(s)
- Gopal S Tandel
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, 440010, India.
| | - Ashish Tiwari
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, 440010, India.
| | - O G Kakde
- Indian Institute of Information Technology, Nagpur, 440006, India.
| |
Collapse
|
28
|
Gu Y, Li K. A Transfer Model Based on Supervised Multi-Layer Dictionary Learning for Brain Tumor MRI Image Recognition. Front Neurosci 2021; 15:687496. [PMID: 34122003 PMCID: PMC8193061 DOI: 10.3389/fnins.2021.687496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 04/19/2021] [Indexed: 11/30/2022] Open
Abstract
Artificial intelligence (AI) is an effective technology for automatic brain tumor MRI image recognition. The training of an AI model requires a large number of labeled data, but medical data needs to be labeled by professional clinicians, which makes data collection complex and expensive. Moreover, a traditional AI model requires that the training data and test data must follow the independent and identically distributed. To solve this problem, we propose a transfer model based on supervised multi-layer dictionary learning (TSMDL) for brain tumor MRI image recognition in this paper. With the help of the knowledge learned from related domains, the goal of this model is to solve the task of transfer learning where the target domain has only a small number of labeled samples. Based on the framework of multi-layer dictionary learning, the proposed model learns the common shared dictionary of source and target domains in each layer to explore the intrinsic connections and shared information between different domains. At the same time, by making full use of the label information of samples, the Laplacian regularization term is introduced to make the dictionary coding of similar samples as close as possible and the dictionary coding of different class samples as different as possible. The recognition experiments on brain MRI image datasets REMBRANDT and Figshare show that the model performs better than competitive state of-the-art methods.
Collapse
Affiliation(s)
- Yi Gu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Kang Li
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| |
Collapse
|
29
|
Gu X, Shen Z, Xue J, Fan Y, Ni T. Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint. Front Neurosci 2021; 15:679847. [PMID: 34122001 PMCID: PMC8193950 DOI: 10.3389/fnins.2021.679847] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 04/09/2021] [Indexed: 11/30/2022] Open
Abstract
Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. In this article, we propose a brain tumor MR image classification method using convolutional dictionary learning with local constraint (CDLLC). Our method integrates the multi-layer dictionary learning into a convolutional neural network (CNN) structure to explore the discriminative information. Encoding a vector on a dictionary can be considered as multiple projections into new spaces, and the obtained coding vector is sparse. Meanwhile, in order to preserve the geometric structure of data and utilize the supervised information, we construct the local constraint of atoms through a supervised k-nearest neighbor graph, so that the discrimination of the obtained dictionary is strong. To solve the proposed problem, an efficient iterative optimization scheme is designed. In the experiment, two clinically relevant multi-class classification tasks on the Cheng and REMBRANDT datasets are designed. The evaluation results demonstrate that our method is effective for brain tumor MR image classification, and it could outperform other comparisons.
Collapse
Affiliation(s)
- Xiaoqing Gu
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Zongxuan Shen
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Jing Xue
- Department of Nephrology, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yiqing Fan
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Tongguang Ni
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| |
Collapse
|
30
|
Barragán-Montero A, Javaid U, Valdés G, Nguyen D, Desbordes P, Macq B, Willems S, Vandewinckele L, Holmström M, Löfman F, Michiels S, Souris K, Sterpin E, Lee JA. Artificial intelligence and machine learning for medical imaging: A technology review. Phys Med 2021; 83:242-256. [PMID: 33979715 PMCID: PMC8184621 DOI: 10.1016/j.ejmp.2021.04.016] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 04/15/2021] [Accepted: 04/18/2021] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.
Collapse
Affiliation(s)
- Ana Barragán-Montero
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium.
| | - Umair Javaid
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| | - Gilmer Valdés
- Department of Radiation Oncology, Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, USA
| | - Paul Desbordes
- Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Belgium
| | - Benoit Macq
- Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Belgium
| | - Siri Willems
- ESAT/PSI, KU Leuven Belgium & MIRC, UZ Leuven, Belgium
| | | | | | | | - Steven Michiels
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| | - Kevin Souris
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| | - Edmond Sterpin
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium; KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Belgium
| | - John A Lee
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| |
Collapse
|
31
|
Nalepa J. AIM and Brain Tumors. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_284-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|