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Abdusalomov A, Rakhimov M, Karimberdiyev J, Belalova G, Cho YI. Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments. Bioengineering (Basel) 2024; 11:627. [PMID: 38927863 PMCID: PMC11201188 DOI: 10.3390/bioengineering11060627] [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: 05/09/2024] [Revised: 06/09/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Medical imaging and deep learning models are essential to the early identification and diagnosis of brain cancers, facilitating timely intervention and improving patient outcomes. This research paper investigates the integration of YOLOv5, a state-of-the-art object detection framework, with non-local neural networks (NLNNs) to improve brain tumor detection's robustness and accuracy. This study begins by curating a comprehensive dataset comprising brain MRI scans from various sources. To facilitate effective fusion, the YOLOv5 and NLNNs, K-means+, and spatial pyramid pooling fast+ (SPPF+) modules are integrated within a unified framework. The brain tumor dataset is used to refine the YOLOv5 model through the application of transfer learning techniques, adapting it specifically to the task of tumor detection. The results indicate that the combination of YOLOv5 and other modules results in enhanced detection capabilities in comparison to the utilization of YOLOv5 exclusively, proving recall rates of 86% and 83% respectively. Moreover, the research explores the interpretability aspect of the combined model. By visualizing the attention maps generated by the NLNNs module, the regions of interest associated with tumor presence are highlighted, aiding in the understanding and validation of the decision-making procedure of the methodology. Additionally, the impact of hyperparameters, such as NLNNs kernel size, fusion strategy, and training data augmentation, is investigated to optimize the performance of the combined model.
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Affiliation(s)
- Akmalbek Abdusalomov
- Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea;
| | - Mekhriddin Rakhimov
- Department of Artificial Intelligence, Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan; (M.R.); (J.K.)
| | - Jakhongir Karimberdiyev
- Department of Artificial Intelligence, Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan; (M.R.); (J.K.)
| | - Guzal Belalova
- Department of Information Systems and Technologies, Tashkent State University of Economics, Tashkent 100066, Uzbekistan;
| | - Young Im Cho
- Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea;
- Department of Information Systems and Technologies, Tashkent State University of Economics, Tashkent 100066, Uzbekistan;
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Batool A, Byun YC. Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions. Comput Biol Med 2024; 175:108412. [PMID: 38691914 DOI: 10.1016/j.compbiomed.2024.108412] [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: 10/19/2023] [Revised: 03/18/2024] [Accepted: 04/02/2024] [Indexed: 05/03/2024]
Abstract
Brain tumor segmentation and classification play a crucial role in the diagnosis and treatment planning of brain tumors. Accurate and efficient methods for identifying tumor regions and classifying different tumor types are essential for guiding medical interventions. This study comprehensively reviews brain tumor segmentation and classification techniques, exploring various approaches based on image processing, machine learning, and deep learning. Furthermore, our study aims to review existing methodologies, discuss their advantages and limitations, and highlight recent advancements in this field. The impact of existing segmentation and classification techniques for automated brain tumor detection is also critically examined using various open-source datasets of Magnetic Resonance Images (MRI) of different modalities. Moreover, our proposed study highlights the challenges related to segmentation and classification techniques and datasets having various MRI modalities to enable researchers to develop innovative and robust solutions for automated brain tumor detection. The results of this study contribute to the development of automated and robust solutions for analyzing brain tumors, ultimately aiding medical professionals in making informed decisions and providing better patient care.
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Affiliation(s)
- Amreen Batool
- Department of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju, 63243, South Korea
| | - Yung-Cheol Byun
- Department of Computer Engineering, Major of Electronic Engineering, Jeju National University, Institute of Information Science Technology, Jeju, 63243, South Korea.
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Kumar PR, Jha RK, Katti A. Brain tissue segmentation in neurosurgery: a systematic analysis for quantitative tractography approaches. Acta Neurol Belg 2024; 124:1-15. [PMID: 36609837 DOI: 10.1007/s13760-023-02170-9] [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: 07/14/2022] [Accepted: 12/31/2022] [Indexed: 01/09/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) is a cutting-edge imaging method that provides a macro-scale in vivo map of the white matter pathways in the brain. The measurement of brain microstructure and the enhancement of tractography rely heavily on dMRI tissue segmentation. Anatomical MRI technique (e.g., T1- and T2-weighted imaging) is the most widely used method for segmentation in dMRI. In comparison to anatomical MRI, dMRI suffers from higher image distortions, lower image quality, and making inter-modality registration more difficult. The dMRI tractography study of brain connectivity has become a major part of the neuroimaging landscape in recent years. In this research, we provide a high-level overview of the methods used to segment several brain tissues types, including grey and white matter and cerebrospinal fluid, to enable quantitative studies of structural connectivity in the brain in health and illness. In the first part of our review, we discuss the three main phases in the quantitative analysis of tractography, which are correction, segmentation, and quantification. Methodological possibilities are described for each phase, along with their popularity and potential benefits and drawbacks. After that, we will look at research that used quantitative tractography approaches to examine the white and grey matter of the brain, with an emphasis on neurodevelopment, ageing, neurological illnesses, mental disorders, and neurosurgery as possible applications. Even though there have been substantial advancements in methodological technology and the spectrum of applications, there is still no consensus regarding the "optimal" approach in the quantitative analysis of tractography. As a result, researchers should tread carefully when interpreting the findings of quantitative analysis of tractography.
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Affiliation(s)
- Puranam Revanth Kumar
- Department of Electronics and Communication Engineering, IcfaiTech (Faculty of Science and Technology), IFHE University, Hyderabad, 501203, India.
| | - Rajesh Kumar Jha
- Department of Electronics and Communication Engineering, IcfaiTech (Faculty of Science and Technology), IFHE University, Hyderabad, 501203, India
| | - Amogh Katti
- Department of Computer Science and Engineering, Gitam School of Technology, GITAM University, Hyderabad, 502329, India
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A survey of deep learning for MRI brain tumor segmentation methods: Trends, challenges, and future directions. HEALTH AND TECHNOLOGY 2023. [DOI: 10.1007/s12553-023-00737-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2023]
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Zahid U, Ashraf I, Khan MA, Alhaisoni M, Yahya KM, Hussein HS, Alshazly H. BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1465173. [PMID: 35965745 PMCID: PMC9371837 DOI: 10.1155/2022/1465173] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/05/2022] [Indexed: 12/14/2022]
Abstract
Early detection of brain tumors can save precious human life. This work presents a fully automated design to classify brain tumors. The proposed scheme employs optimal deep learning features for the classification of FLAIR, T1, T2, and T1CE tumors. Initially, we normalized the dataset to pass them to the ResNet101 pretrained model to perform transfer learning for our dataset. This approach results in fine-tuning the ResNet101 model for brain tumor classification. The problem with this approach is the generation of redundant features. These redundant features degrade accuracy and cause computational overhead. To tackle this problem, we find optimal features by utilizing differential evaluation and particle swarm optimization algorithms. The obtained optimal feature vectors are then serially fused to get a single-fused feature vector. PCA is applied to this fused vector to get the final optimized feature vector. This optimized feature vector is fed as input to various classifiers to classify tumors. Performance is analyzed at various stages. Performance results show that the proposed technique achieved a speedup of 25.5x in prediction time on the medium neural network with an accuracy of 94.4%. These results show significant improvement over the state-of-the-art techniques in terms of computational overhead by maintaining approximately the same accuracy.
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Affiliation(s)
- Usman Zahid
- Department of Computer Engineering, HITEC University, Taxila 47080, Pakistan
| | - Imran Ashraf
- Department of Computer Engineering, HITEC University, Taxila 47080, Pakistan
| | | | - Majed Alhaisoni
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Khawaja M. Yahya
- Department of Electrical Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Hany S. Hussein
- Electrical Engineering Department, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia
- Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt
| | - Hammam Alshazly
- Faculty of Computers and Information, South Valley University, Qena 83523, Egypt
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Kouli O, Hassane A, Badran D, Kouli T, Hossain-Ibrahim K, Steele JD. Automated brain tumour identification using magnetic resonance imaging: a systematic review and meta-analysis. Neurooncol Adv 2022; 4:vdac081. [PMID: 35769411 PMCID: PMC9234754 DOI: 10.1093/noajnl/vdac081] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background Automated brain tumor identification facilitates diagnosis and treatment planning. We evaluate the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation, using MRI. Methods A systematic literature search from January 2000 to May 8, 2021 was conducted. Study quality was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Detection meta-analysis was performed using a unified hierarchical model. Segmentation studies were evaluated using a random effects model. Sensitivity analysis was performed for externally validated studies. Results Of 224 studies included in the systematic review, 46 segmentation and 38 detection studies were eligible for meta-analysis. In detection, DL achieved a lower false positive rate compared to TML; 0.018 (95% CI, 0.011 to 0.028) and 0.048 (0.032 to 0.072) (P < .001), respectively. In segmentation, DL had a higher dice similarity coefficient (DSC), particularly for tumor core (TC); 0.80 (0.77 to 0.83) and 0.63 (0.56 to 0.71) (P < .001), persisting on sensitivity analysis. Both manual and automated whole tumor (WT) segmentation had “good” (DSC ≥ 0.70) performance. Manual TC segmentation was superior to automated; 0.78 (0.69 to 0.86) and 0.64 (0.53 to 0.74) (P = .014), respectively. Only 30% of studies reported external validation. Conclusions The comparable performance of automated to manual WT segmentation supports its integration into clinical practice. However, manual outperformance for sub-compartmental segmentation highlights the need for further development of automated methods in this area. Compared to TML, DL provided superior performance for detection and sub-compartmental segmentation. Improvements in the quality and design of studies, including external validation, are required for the interpretability and generalizability of automated models.
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Affiliation(s)
- Omar Kouli
- School of Medicine, University of Dundee , Dundee UK
- NHS Greater Glasgow and Clyde , Dundee UK
| | | | | | - Tasnim Kouli
- School of Medicine, University of Dundee , Dundee UK
| | | | - J Douglas Steele
- Division of Imaging Science and Technology, School of Medicine, University of Dundee , UK
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Majanga V, Viriri S. A Survey of Dental Caries Segmentation and Detection Techniques. ScientificWorldJournal 2022; 2022:8415705. [PMID: 35450417 PMCID: PMC9017544 DOI: 10.1155/2022/8415705] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/21/2022] [Accepted: 03/10/2022] [Indexed: 01/15/2023] Open
Abstract
Dental caries detection, in the past, has been a challenging task given the amount of information got from various radiographic images. Several methods have been introduced to improve the quality of images for faster caries detection. Deep learning has become the methodology of choice when it comes to analysis of medical images. This survey gives an in-depth look into the use of deep learning for object detection, segmentation, and classification. It further looks into literature on segmentation and detection methods of dental images through deep learning. From the literature studied, we found out that methods were grouped according to the type of dental caries (proximal, enamel), type of X-ray images used (extraoral, intraoral), and segmentation method (threshold-based, cluster-based, boundary-based, and region-based). From the works reviewed, the main focus has been found to be on threshold-based segmentation methods. Most of the reviewed papers have preferred the use of intraoral X-ray images over extraoral X-ray images to perform segmentation on dental images of already isolated parts of the teeth. This paper presents an in-depth analysis of recent research in deep learning for dental caries segmentation and detection. It involves discussing the methods and algorithms used in segmenting and detecting dental caries. It also discusses various existing models used and how they compare with each other in terms of system performance and evaluation. We also discuss the limitations of these methods, as well as future perspectives on how to improve their performance.
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Affiliation(s)
- Vincent Majanga
- Statistics and Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa
| | - Serestina Viriri
- Statistics and Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa
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Saeidifar M, Yazdi M, Zolghadrasli A. Performance Improvement in Brain Tumor Detection in MRI Images Using a Combination of Evolutionary Algorithms and Active Contour Method. J Digit Imaging 2021; 34:1209-1224. [PMID: 34561783 DOI: 10.1007/s10278-021-00514-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 08/23/2021] [Accepted: 08/31/2021] [Indexed: 10/20/2022] Open
Abstract
The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an automatic tumor detection algorithm to assist radiologists and physicians for brain tumor diagnosis. The aim of the present study is to automatically detect the location of the tumor in a brain MRI image with high accuracy. For this end, in the proposed algorithm, first, the skull is separated from the brain using morphological operators. The image is then segmented by six evolutionary algorithms, i.e., Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), and Gray Wolf Optimization (GWO), as well as two other frequently-used techniques in the literature, i.e., K-means and Otsu thresholding algorithms. Afterwards, the tumor area is isolated from the brain using the four features extracted from the main tumor. Evaluation of the segmented area revealed that the PSO has the best performance compared with the other approaches. The segmented results of the PSO are then used as the initial curve for the Active contour to precisely specify the tumor boundaries. The proposed algorithm is applied on fifty images with two different types of tumors. Experimental results on T1-weighted brain MRI images show a better performance of the proposed algorithm compared to other evolutionary algorithms, K-means, and Otsu thresholding methods.
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Affiliation(s)
- Mahtab Saeidifar
- School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Mehran Yazdi
- School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
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Latif G, Alghazo J, Sibai FN, Iskandar DNFA, Khan AH. Recent Advancements in Fuzzy C-means Based Techniques for Brain MRI Segmentation. Curr Med Imaging 2021; 17:917-930. [PMID: 33397241 DOI: 10.2174/1573405616666210104111218] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 10/08/2020] [Accepted: 11/12/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. OBJECTIVE In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. RESULTS FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. CONCLUSION In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.
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Affiliation(s)
- Ghazanfar Latif
- College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
| | - Jaafar Alghazo
- College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
| | - Fadi N Sibai
- College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
| | - D N F Awang Iskandar
- Faculty of Computer Science and Information Technology, Universiti Malaysia, Sarawak, Malaysia
| | - Adil H Khan
- Department of Electrical Engineering, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
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Almijalli M, Saad A, Alhussaini K, Aleid A, Alwasel A. Towards Drug Delivery Control Using Iron Oxide Nanoparticles in Three-Dimensional Magnetic Resonance Imaging. NANOMATERIALS 2021; 11:nano11081876. [PMID: 34443707 PMCID: PMC8401072 DOI: 10.3390/nano11081876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/14/2021] [Accepted: 07/20/2021] [Indexed: 11/16/2022]
Abstract
The purpose of this paper was to detect and separate the cluster intensity provided by Iron oxide nanoparticles (IO-NPs), in the MRI images, to investigate the drug delivery effectiveness. IO-NPs were attached to the macrophages and inserted into the eye of the inflamed mouse’s calf. The low resolution of MRI and the tiny dimension of the IO-NPs made the situation challenging. IO-NPs serve as a marker, due to their strong intensity in the MRI, enabling us to follow the track of the macrophages. An image processing procedure was developed to estimate the position and the amount of IO-NPs spreading inside the inflamed mouse leg. A fuzzy Clustering algorithm was adopted to select the region of interest (ROI). A 3D model of the femoral region was used for the detection and then the extraction IO-NPs in the MRI images. The results achieved prove the effectiveness of the proposed method to improve the control process of targeted drug delivered. It helps in optimizing the treatment and opens a promising novel research axis for nanomedicine applications.
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Affiliation(s)
| | - Ali Saad
- Correspondence: ; Tel.: +966-508975969
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Morphological active contour model for automatic brain tumor extraction from multimodal magnetic resonance images. J Neurosci Methods 2021; 362:109296. [PMID: 34302860 DOI: 10.1016/j.jneumeth.2021.109296] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Revised: 07/18/2021] [Accepted: 07/19/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Brain tumor extraction from magnetic resonance (MR) images is challenging due to variations in the location, shape, size and intensity of tumors. Manual delineation of brain tumors from MR images is time-consuming and prone to human errors. METHOD In this paper, we present a method for automatic tumor extraction from multimodal MR images. Brain tumors are first detected using k-means clustering. A morphological region-based active contour model is then used for tumor extraction using an initial contour defined based on the boundary of the detected brain tumor regions. The contour evolution for tumor extraction was performed using successive application of morphological operators. In our model, a Gaussian distribution was used to model local image intensities. The spatial correlation between neighboring voxels was also modeled using Markov random field. RESULTS The proposed method was evaluated on BraTS 2013 dataset including patients with high-grade and low-grade tumors. In comparison with other active contour based methods, the proposed method yielded better performance on tumor segmentation with mean Dice similarity coefficients of 0.9179 ( ± 0.025) and 0.8910 ( ± 0.042) obtained on high-grade and low-grade tumors, respectively. CONCLUSION The proposed method achieved higher accuracies for brain tumor extraction in comparison to other contour-based methods.
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Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102458] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, Foran D, Do N, Golemati S, Kurc T, Huang K, Nikita KS, Veasey BP, Zervakis M, Saltz JH, Pattichis CS. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform 2020; 24:1837-1857. [PMID: 32609615 PMCID: PMC8580417 DOI: 10.1109/jbhi.2020.2991043] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.
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Chandra SK, Bajpai MK. Fractional Crank-Nicolson finite difference method for benign brain tumor detection and segmentation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Aruna SK, Sindhanaiselvan K, Madhusudhanan B. Computerized grading of brain tumors supplemented by artificial intelligence. Soft comput 2020. [DOI: 10.1007/s00500-019-04403-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Chandra SK, Bajpai MK. Fractional mesh-free linear diffusion method for image enhancement and segmentation for automatic tumor classification. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101841] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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17
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A review on brain tumor segmentation of MRI images. Magn Reson Imaging 2019; 61:247-259. [DOI: 10.1016/j.mri.2019.05.043] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 05/30/2019] [Accepted: 05/30/2019] [Indexed: 01/17/2023]
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Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area. Int J Biomed Imaging 2019; 2019:1758948. [PMID: 30941165 PMCID: PMC6421017 DOI: 10.1155/2019/1758948] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 01/31/2019] [Accepted: 02/06/2019] [Indexed: 11/17/2022] Open
Abstract
Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan–Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.
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Magnetic Resonance Imaging Segmentation Techniques of Brain Tumors: A Review. ARCHIVES OF NEUROSCIENCE 2019. [DOI: 10.5812/ans.84920] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Abiwinanda N, Hanif M, Hesaputra ST, Handayani A, Mengko TR. Brain Tumor Classification Using Convolutional Neural Network. IFMBE PROCEEDINGS 2019. [DOI: 10.1007/978-981-10-9035-6_33] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Ibrahim RW, Hasan AM, Jalab HA. A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 163:21-28. [PMID: 30119853 DOI: 10.1016/j.cmpb.2018.05.031] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Revised: 05/17/2018] [Accepted: 05/24/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVES The MRI brain tumors segmentation is challenging due to variations in terms of size, shape, location and features' intensity of the tumor. Active contour has been applied in MRI scan image segmentation due to its ability to produce regions with boundaries. The main difficulty that encounters the active contour segmentation is the boundary tracking which is controlled by minimization of energy function for segmentation. Hence, this study proposes a novel fractional Wright function (FWF) as a minimization of energy technique to improve the performance of active contour without edge method. METHOD In this study, we implement FWF as an energy minimization function to replace the standard gradient-descent method as minimization function in Chan-Vese segmentation technique. The proposed FWF is used to find the boundaries of an object by controlling the inside and outside values of the contour. In this study, the objective evaluation is used to distinguish the differences between the processed segmented images and ground truth using a set of statistical parameters; true positive, true negative, false positive, and false negative. RESULTS The FWF as a minimization of energy was successfully implemented on BRATS 2013 image dataset. The achieved overall average sensitivity score of the brain tumors segmentation was 94.8 ± 4.7%. CONCLUSIONS The results demonstrate that the proposed FWF method minimized the energy function more than the gradient-decent method that was used in the original three-dimensional active contour without edge (3DACWE) method.
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Affiliation(s)
- Rabha W Ibrahim
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia.
| | - Ali M Hasan
- College of Medicine, Al-Nahrain University, Baghdad, Iraq.
| | - Hamid A Jalab
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia.
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A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis. Symmetry (Basel) 2018. [DOI: 10.3390/sym10070270] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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