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Basha NK, Ananth C, Muthukumaran K, Sudhamsu G, Mittal V, Gared F. Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation. Sci Rep 2024; 14:17615. [PMID: 39080324 PMCID: PMC11289405 DOI: 10.1038/s41598-024-66554-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 07/02/2024] [Indexed: 08/02/2024] Open
Abstract
The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convolutional Neural Network (R-CNN) masks, Grad-CAM and transfer learning, this work offers an effective method for the detection of brain tumours. Helping doctors make extremely accurate diagnoses is the goal. A transfer learning-based model has been suggested that offers high sensitivity and accuracy scores for brain tumour detection when segmentation is done using R-CNN masks. To train the model, the Inception V3, VGG-16, and ResNet-50 architectures were utilised. The Brain MRI Images for Brain Tumour Detection dataset was utilised to develop this method. This work's performance is evaluated and reported in terms of recall, specificity, sensitivity, accuracy, precision, and F1 score. A thorough analysis has been done comparing the proposed model operating with three distinct architectures: VGG-16, Inception V3, and Resnet-50. Comparing the proposed model, which was influenced by the VGG-16, to related works also revealed its performance. Achieving high sensitivity and accuracy percentages was the main goal. Using this approach, an accuracy and sensitivity of around 99% were obtained, which was much greater than current efforts.
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Affiliation(s)
- Niha Kamal Basha
- School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
| | - Christo Ananth
- Samarkand State University, Samarkand, Uzbekistan
- Samarkand branch, Tashkent State University of Economics, Samarkand, Uzbekistan
| | - K Muthukumaran
- Department of BioMedical Engineering, Dhanalakshmi Srinivasan College of Engineering and Technology, Anna University, Chennai, Tamilnadu, India
| | - Gadug Sudhamsu
- Department of Computer Science and Engineering, School of Engineering and Technology, JAIN University, Bangalore, Karnataka, India
| | - Vikas Mittal
- Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab, 140413, India
| | - Fikreselam Gared
- Faculty of Electrical and Computer Engineering, Bahir Dar University, Bahir Dar, Ethiopia.
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Kiaei DS, El-Jalbout R, Décarie JC, Perreault S, Dehaes M. Development of a semi-automatic segmentation technique based on mean magnetic resonance imaging intensity thresholding for volumetric quantification of plexiform neurofibromas. Heliyon 2024; 10:e23445. [PMID: 38173515 PMCID: PMC10761559 DOI: 10.1016/j.heliyon.2023.e23445] [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: 08/25/2023] [Revised: 12/01/2023] [Accepted: 12/04/2023] [Indexed: 01/05/2024] Open
Abstract
Rationale and objectives Plexiform neurofibromas (PNs) are peripheral nerve tumors that occur in 25-50 % of patients with neurofibromatosis type 1. PNs may have complex, diffused, and irregular shapes. The objective of this work was to develop a volumetric quantification method for PNs as clinical assessment is currently based on unidimensional measurement. Materials and methods A semi-automatic segmentation technique based on mean magnetic resonance imaging (MRI) intensity thresholding (SSTMean) was developed and compared to a similar and previously published technique based on minimum image intensity thresholding (SSTMini). The performance (volume and computation time) of the two techniques was compared to manual tracings of 15 tumors of different locations, shapes, and sizes. Performance was also assessed using different MRI sequences. Reproducibility was assessed by inter-observer analysis. Results When compared to manual tracing, quantification performed with SSTMean was not significantly different (mean difference: 1.2 %), while volumes computed by SSTMini were significantly different (p < .0001, mean difference: 13.4 %). Volumes quantified by SSTMean were also significantly different than the ones assessed by SSTMini (p < .0001). Using SSTMean, volumes quantified with short TI inversion recovery, T1-, and T2-weighted imaging were not significantly different. Computation times used by SSTMean and SSTMini were significantly lower than for manual segmentation (p < .0001). The highest difference measured by two users was 8 cm3. Conclusion Our method showed accuracy compared to a current gold standard (manual tracing) and reproducibility between users. The refined segmentation threshold and the possibility to define multiple regions-of-interest to initiate segmentation may have contributed to its performance. The versatility and speed of our method may prove useful to better monitor volumetric changes in lesions of patients enrolled in clinical trials to assessing response to therapy.
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Affiliation(s)
- Dorsa Sadat Kiaei
- Institute of Biomedical Engineering, University of Montréal, Montréal, Canada
- Research Center, CHU Sainte-Justine Hospital University Centre, Montréal, Canada
| | - Ramy El-Jalbout
- Research Center, CHU Sainte-Justine Hospital University Centre, Montréal, Canada
- Department of Radiology, Radio-oncology and Nuclear Medicine, University of Montreal, Montreal, Canada
| | - Jean-Claude Décarie
- Department of Radiology, Radio-oncology and Nuclear Medicine, University of Montreal, Montreal, Canada
| | - Sébastien Perreault
- Research Center, CHU Sainte-Justine Hospital University Centre, Montréal, Canada
- Department of Neurosciences, University of Montreal, Montreal, Canada
| | - Mathieu Dehaes
- Institute of Biomedical Engineering, University of Montréal, Montréal, Canada
- Research Center, CHU Sainte-Justine Hospital University Centre, Montréal, Canada
- Department of Radiology, Radio-oncology and Nuclear Medicine, University of Montreal, Montreal, Canada
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Shen L, Zhang Y, Wang Q, Qin F, Sun D, Min H, Meng Q, Xu C, Zhao W, Song X. Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation. PLoS One 2023; 18:e0288658. [PMID: 37440581 DOI: 10.1371/journal.pone.0288658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical planning is required. However, it is a challenge to use deep learning to achieve accurate segmentation of medical images due to the diversity of tumors and the complex boundary interactions between sub-regions while limited computing resources hinder the construction of efficient neural networks. We propose a feature fusion module based on a hierarchical decoupling convolution network and an attention mechanism to improve the performance of network segmentation. We replaced the skip connections of U-shaped networks with a feature fusion module to solve the category imbalance problem, thus contributing to the segmentation of more complicated medical images. We introduced a global attention mechanism to further integrate the features learned by the encoder and explore the context information. The proposed method was evaluated for enhance tumor, whole tumor, and tumor core, achieving Dice similarity coefficient metrics of 0.775, 0.900, and 0.827, respectively, on the BraTS 2019 dataset and 0.800, 0.902, and 0.841, respectively on the BraTS 2018 dataset. The results show that our proposed method is inherently general and is a powerful tool for brain tumor image studies. Our code is available at: https://github.com/WSake/Feature-interaction-network-based-on-Hierarchical-Decoupled-Convolution.
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Affiliation(s)
- Longfeng Shen
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Big-Data Research Center on University Management, Huaibei, Anhui, China
| | - Yingjie Zhang
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
| | - Qiong Wang
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
| | - Fenglan Qin
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
| | - Dengdi Sun
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computing, School of Artificial Intelligence, Anhui University, Hefei, China
| | - Hai Min
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China
| | - Qianqian Meng
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
| | - Chengzhen Xu
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Wei Zhao
- Huaibei People's Hospital, Huaibei, Anhui, China
| | - Xin Song
- Huaibei People's Hospital, Huaibei, Anhui, China
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Darwish SM, Abu Shaheen LJ, Elzoghabi AA. A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique. Bioengineering (Basel) 2023; 10:819. [PMID: 37508846 PMCID: PMC10376225 DOI: 10.3390/bioengineering10070819] [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: 06/05/2023] [Revised: 06/29/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm's mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS' 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.
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Affiliation(s)
- Saad M Darwish
- Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El Shatby, Alexandria 21526, Egypt
| | - Lina J Abu Shaheen
- Department of Computer Information Systems, College of Technology and Applied Sciences, Al-Quds Open University, Deir AL Balah P920, Palestine
| | - Adel A Elzoghabi
- Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El Shatby, Alexandria 21526, Egypt
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Luo J, Pan M, Mo K, Mao Y, Zou D. Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma. Semin Cancer Biol 2023; 91:110-123. [PMID: 36907387 DOI: 10.1016/j.semcancer.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 03/05/2023] [Accepted: 03/08/2023] [Indexed: 03/12/2023]
Abstract
Glioma represents a dominant primary intracranial malignancy in the central nervous system. Artificial intelligence that mainly includes machine learning, and deep learning computational approaches, presents a unique opportunity to enhance clinical management of glioma through improving tumor segmentation, diagnosis, differentiation, grading, treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular features, clinical classification, characterization of the tumor microenvironment, and drug discovery. A growing body of recent studies apply artificial intelligence-based models to disparate data sources of glioma, covering imaging modalities, digital pathology, high-throughput multi-omics data (especially emerging single-cell RNA sequencing and spatial transcriptome), etc. While these early findings are promising, future studies are required to normalize artificial intelligence-based models to improve the generalizability and interpretability of the results. Despite prominent issues, targeted clinical application of artificial intelligence approaches in glioma will facilitate the development of precision medicine of this field. If these challenges can be overcome, artificial intelligence has the potential to profoundly change the way patients with or at risk of glioma are provided with more rational care.
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Affiliation(s)
- Jiefeng Luo
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Mika Pan
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Ke Mo
- Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Yingwei Mao
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.
| | - Donghua Zou
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China; Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China.
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Sasmal B, Dhal KG. A survey on the utilization of Superpixel image for clustering based image segmentation. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-63. [PMID: 37362658 PMCID: PMC9992924 DOI: 10.1007/s11042-023-14861-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 06/22/2022] [Accepted: 02/06/2023] [Indexed: 06/28/2023]
Abstract
Superpixel become increasingly popular in image segmentation field as it greatly helps image segmentation techniques to segment the region of interest accurately in noisy environment and also reduces the computation effort to a great extent. However, selection of proper superpixel generation techniques and superpixel image segmentation techniques play a very crucial role in the domain of different kinds of image segmentation. Clustering is a well-accepted image segmentation technique and proved their effective performance over various image segmentation field. Therefore, this study presents an up-to-date survey on the employment of superpixel image in combined with clustering techniques for the various image segmentation. The contribution of the survey has four parts namely (i) overview of superpixel image generation techniques, (ii) clustering techniques especially efficient partitional clustering techniques, their issues and overcoming strategies, (iii) Review of superpixel combined with clustering strategies exist in literature for various image segmentation, (iv) lastly, the comparative study among superpixel combined with partitional clustering techniques has been performed over oral pathology and leaf images to find out the efficacy of the combination of superpixel and partitional clustering approaches. Our evaluations and observation provide in-depth understanding of several superpixel generation strategies and how they apply to the partitional clustering method.
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Affiliation(s)
- Buddhadev Sasmal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
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Ramprasad MVS, Rahman MZU, Bayleyegn MD. A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:178-188. [PMID: 36712319 PMCID: PMC9870266 DOI: 10.1109/ojemb.2022.3217186] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/14/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022] Open
Abstract
Goal: Implementation of an artificial intelli gence-based medical diagnosis tool for brain tumor classification, which is called the BTFSC-Net. Methods: Medical images are preprocessed using a hybrid probabilistic wiener filter (HPWF) The deep learning convolutional neural network (DLCNN) was utilized to fuse MRI and CT images with robust edge analysis (REA) properties, which are used to identify the slopes and edges of source images. Then, hybrid fuzzy c-means integrated k-means (HFCMIK) clustering is used to segment the disease affected region from the fused image. Further, hybrid features such as texture, colour, and low-level features are extracted from the fused image by using gray-level cooccurrence matrix (GLCM), redundant discrete wavelet transform (RDWT) descriptors. Finally, a deep learning based probabilistic neural network (DLPNN) is used to classify malignant and benign tumors. The BTFSC-Net attained 99.21% of segmentation accuracy and 99.46% of classification accuracy. Conclusions: The simulations showed that BTFSC-Net outperformed as compared to existing methods.
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Affiliation(s)
- M V S Ramprasad
- Koneru Lakshmaiah Education FoundationK L University Guntur 522302 India
- GITAM (Deemed to be University) Visakhapatnam AP 522502 India
| | - Md Zia Ur Rahman
- Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education FoundationK L University Vaddeswaram Guntur 522502 India
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Hussain S, Xi X, Ullah I, Inam SA, Naz F, Shaheed K, Ali SA, Tian C. A Discriminative Level Set Method with Deep Supervision for Breast Tumor Segmentation. Comput Biol Med 2022; 149:105995. [DOI: 10.1016/j.compbiomed.2022.105995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 08/05/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
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Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation. Cancers (Basel) 2022; 14:cancers14184399. [PMID: 36139559 PMCID: PMC9496881 DOI: 10.3390/cancers14184399] [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: 07/12/2022] [Revised: 09/04/2022] [Accepted: 09/07/2022] [Indexed: 11/29/2022] Open
Abstract
Simple Summary Segmentation of brain tumor images from magnetic resonance imaging (MRI) is a challenging topic in medical image analysis. The brain tumor can take many shapes, and MRI images vary considerably in intensity, making lesion detection difficult for radiologists. This paper proposes a three-step approach to solving this problem: (1) pre-processing, based on morphological operations, is applied to remove the skull bone from the image; (2) the particle swarm optimization (PSO) algorithm, with a two-way fixed-effects analysis of variance (ANOVA)-based fitness function, is used to find the optimal block containing the brain lesion; (3) the K-means clustering algorithm is adopted, to classify the detected block as tumor or non-tumor. An extensive experimental analysis, including visual and statistical evaluations, was conducted, using two MRI databases: a private database provided by the Kouba imaging center—Algiers (KICA)—and the multimodal brain tumor segmentation challenge (BraTS) 2015 database. The results show that the proposed methodology achieved impressive performance, compared to several competing approaches. Abstract Segmentation of brain tumor images, to refine the detection and understanding of abnormal masses in the brain, is an important research topic in medical imaging. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed, to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied, to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block, to classify it as a tumor or not. A thorough evaluation of the proposed algorithm was performed, using: (1) a private MRI database provided by the Kouba imaging center—Algiers (KICA); (2) the multimodal brain tumor segmentation challenge (BraTS) 2015 database. Estimates of the selected fitness function were first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion, to demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm was then compared to the results of several state-of-the-art techniques. The results obtained, by using the Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements, demonstrated the superiority of the proposed optimized segmentation algorithm over equivalent techniques.
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Xu J, Meng Y, Qiu K, Topatana W, Li S, Wei C, Chen T, Chen M, Ding Z, Niu G. Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges. Front Oncol 2022; 12:892056. [PMID: 35965542 PMCID: PMC9363668 DOI: 10.3389/fonc.2022.892056] [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: 03/24/2022] [Accepted: 06/22/2022] [Indexed: 12/24/2022] Open
Abstract
Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.
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Affiliation(s)
- Jiaona Xu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuting Meng
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kefan Qiu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Win Topatana
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shijie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Wei
- Department of Neurology, Affiliated Ningbo First Hospital, Ningbo, China
| | - Tianwen Chen
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mingyu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Guozhong Niu
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
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Kaur R, Khehra BS. Automated Knowledge Extraction of Liver Cysts From CT Images Using Modified Whale Optimization and Fuzzy C Means Clustering Algorithm. INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN 2022. [DOI: 10.4018/ijismd.306644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this study, the integrated modified whale optimization and modified fuzzy c-means clustering algorithm using morphological operations are developed and implemented for appropriate knowledge extraction of a cyst from computer tomography (CT) images of the liver to facilitate modern intelligent healthcare systems. The proposed approach plays an efficient role in diagnosing the liver cyst. To evaluate the efficiency, the outcomes of the proposed approach have been compared with the minimum cross entropy based modified whale optimization algorithm (MCE and MWOA), teaching-learning optimization algorithm based upon minimum cross entropy (MCE and TLBO), particle swarm intelligence algorithm (PSO), genetic algorithm (GA), differential evolution (DE) algorithm, and k-means clustering algorithm. For this, various parameters such as uniformity (U), mean structured similarity index (MSSIM), structured similarity index (SSIM), random index (RI), and peak signal-to-noise ratio (PSNR) have been considered. The experimental results show that the proposed approach is more efficient and accurate than others.
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Affiliation(s)
- Ramanjot Kaur
- Department of Computer Science and Engineering, I.K. Gujral Punjab Technical University, Jalandhar, India
| | - Baljit Singh Khehra
- Department of Computer Science and Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, India
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Huang F, Noël R, Berg P, Hosseini SA. Simulation of the FDA nozzle benchmark: A lattice Boltzmann study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106863. [PMID: 35617810 DOI: 10.1016/j.cmpb.2022.106863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 04/20/2022] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Contrary to flows in small intracranial vessels, many blood flow configurations such as those found in aortic vessels and aneurysms involve larger Reynolds numbers and, therefore, transitional or turbulent conditions. Dealing with such systems require both robust and efficient numerical methods. METHODS We assess here the performance of a lattice Boltzmann solver with full Hermite expansion of the equilibrium and central Hermite moments collision operator at higher Reynolds numbers, especially for under-resolved simulations. To that end the food and drug administration's benchmark nozzle is considered at three different Reynolds numbers covering all regimes: (1) laminar at a Reynolds number of 500, (2) transitional at a Reynolds number of 3500, and (3) low-level turbulence at a Reynolds number of 6500. RESULTS The lattice Boltzmann results are compared with previously published inter-laboratory experimental data obtained by particle image velocimetry. Our results show good agreement with the experimental measurements throughout the nozzle, demonstrating the good performance of the solver even in under-resolved simulations. CONCLUSION In this manner, fast but sufficiently accurate numerical predictions can be achieved for flow configurations of practical interest regarding medical applications.
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Affiliation(s)
- Feng Huang
- Laboratory of Fluid Dynamics and Technical Flows, University of Magdeburg "Otto von Guericke", Magdeburg D-39106, Germany
| | - Romain Noël
- Univ. Gustave Eiffel, Inria, Cosys/SII, I4S, Bouguenais F-44344, France
| | - Philipp Berg
- Laboratory of Fluid Dynamics and Technical Flows, University of Magdeburg "Otto von Guericke", Magdeburg D-39106, Germany; Research Campus STIMULATE, University of Magdeburg "Otto von Guericke", Magdeburg, D-39106, Germany
| | - Seyed Ali Hosseini
- Laboratory of Fluid Dynamics and Technical Flows, University of Magdeburg "Otto von Guericke", Magdeburg D-39106, Germany; Department of Mechanical and Process Engineering, ETH Zürich, Zürich 8092, Switzerland.
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Zhou T, Vera P, Canu S, Ruan S. Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Low-contrast lesion segmentation in advanced MRI experiments by time-domain Ricker-type wavelets and fuzzy 2-means. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03184-1] [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]
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15
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Ilesanmi AE, Chaumrattanakul U, Makhanov SS. Methods for the segmentation and classification of breast ultrasound images: a review. J Ultrasound 2021; 24:367-382. [PMID: 33428123 PMCID: PMC8572242 DOI: 10.1007/s40477-020-00557-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 12/21/2020] [Indexed: 02/07/2023] Open
Abstract
PURPOSE Breast ultrasound (BUS) is one of the imaging modalities for the diagnosis and treatment of breast cancer. However, the segmentation and classification of BUS images is a challenging task. In recent years, several methods for segmenting and classifying BUS images have been studied. These methods use BUS datasets for evaluation. In addition, semantic segmentation algorithms have gained prominence for segmenting medical images. METHODS In this paper, we examined different methods for segmenting and classifying BUS images. Popular datasets used to evaluate BUS images and semantic segmentation algorithms were examined. Several segmentation and classification papers were selected for analysis and review. Both conventional and semantic methods for BUS segmentation were reviewed. RESULTS Commonly used methods for BUS segmentation were depicted in a graphical representation, while other conventional methods for segmentation were equally elucidated. CONCLUSIONS We presented a review of the segmentation and classification methods for tumours detected in BUS images. This review paper selected old and recent studies on segmenting and classifying tumours in BUS images.
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Affiliation(s)
- Ademola E. Ilesanmi
- School of ICT, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12000 Thailand
| | | | - Stanislav S. Makhanov
- School of ICT, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12000 Thailand
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Superpixel Segmentation Based on Grid Point Density Peak Clustering. SENSORS 2021; 21:s21196374. [PMID: 34640692 PMCID: PMC8512046 DOI: 10.3390/s21196374] [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: 08/18/2021] [Revised: 09/14/2021] [Accepted: 09/22/2021] [Indexed: 11/17/2022]
Abstract
Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem. That would produce redundant complex jagged textures. In this paper, the density peak clustering will be used to reduce the redundant superpixels and highlight the primary textures and contours of the salient objects. Firstly, the grid pixels are extracted as feature points, and the density of each feature point will be defined. Secondly, the cluster centers are extracted with the density peaks. Finally, all the feature points will be clustered by the density peaks. The pixel blocks, which are obtained by the above steps, are superpixels. The method is carried out in the BSDS500 dataset, and the experimental results show that the Boundary Recall (BR) and Achievement Segmentation Accuracy (ASA) are 95.0% and 96.3%, respectively. In addition, the proposed method has better performance in efficiency (30 fps). The comparison experiments show that not only do the superpixel boundaries have good adhesion to the primary textures and contours of the salient objects, but they can also effectively reduce the redundant superpixels in the homogeneous region.
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Zhang W, Wu Y, Yang B, Hu S, Wu L, Dhelimd S. Overview of Multi-Modal Brain Tumor MR Image Segmentation. Healthcare (Basel) 2021; 9:1051. [PMID: 34442188 PMCID: PMC8392341 DOI: 10.3390/healthcare9081051] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 08/08/2021] [Accepted: 08/10/2021] [Indexed: 11/17/2022] Open
Abstract
The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.
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Affiliation(s)
- Wenyin Zhang
- School of Information Science and Engineering, Linyi University, Linyi 276000, China; (W.Z.); (S.H.)
| | - Yong Wu
- School of Information Science and Engineering, Linyi University, Linyi 276000, China; (W.Z.); (S.H.)
| | - Bo Yang
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan 250022, China;
| | - Shunbo Hu
- School of Information Science and Engineering, Linyi University, Linyi 276000, China; (W.Z.); (S.H.)
| | - Liang Wu
- School of Control Science and Engineering, Shandong University, Jinan 250061, China;
| | - Sahraoui Dhelimd
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;
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Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep 2021; 11:10930. [PMID: 34035406 PMCID: PMC8149837 DOI: 10.1038/s41598-021-90428-8] [Citation(s) in RCA: 99] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 05/07/2021] [Indexed: 12/15/2022] Open
Abstract
Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.
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A level set method based on domain transformation and bias correction for MRI brain tumor segmentation. J Neurosci Methods 2021; 352:109091. [PMID: 33515604 DOI: 10.1016/j.jneumeth.2021.109091] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 01/18/2021] [Accepted: 01/21/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Intensity inhomogeneity is one of the common artifacts in image processing. This artifact makes image segmentation more challenging and adversely affects the performance of intensity-based image processing algorithms. NEW METHOD In this paper, a novel region-based level set method is proposed for segmenting the images with intensity inhomogeneity with applications to brain tumor segmentation in magnetic resonance imaging (MRI) scans. For this purpose, the inhomogeneous regions are first modeled as Gaussian distributions with different means and variances, and then transferred into a new domain, where preserves the Gaussian intensity distribution of each region but with better separation. Moreover, our method can perform bias field correction. To this end, the bias field is represented by a linear combination of smooth base functions that enables better intensity inhomogeneity modeling. Therefore, level set fundamental formulation and bias field are modified in the proposed approach. RESULTS To assess the performance of the proposed method, different inhomogeneous images, including synthetic images as well as real brain magnetic resonance images from BraTS 2017 dataset are segmented. Being evaluated by Dice, Jaccard, Sensitivity, and Specificity metrics, the results show that the proposed method suppresses the side effect of the over-smoothing object boundary and it has good accuracy in the segmentation of images with extreme intensity non-uniformity. The mean values of these metrics in brain tumor segmentation are 0.86 ± 0.03, 0.77 ± 0.05, 0.94 ± 0.04, 0.99 ± 0.003, respectively. COMPARISON WITH EXISTING METHOD(S) Our method were compared with six state-of-the-art image segmentation methods: Chan-Vese (CV), Local Intensity Clustering (LIC), Local iNtensity Clustering (LINC), Global inhomogeneous intensity clustering (GINC), Multiplicative Intrinsic Component Optimization (MICO), and Local Statistical Active Contour Model (LSACM) models. We used qualitative and quantitative comparison methods for segmenting synthetic and real images. Experiments indicate that our proposed method is robust to noise and intensity non-uniformity and outperforms other state-of-the-art segmentation methods in terms of bias field correction, noise resistance, and segmentation accuracy. CONCLUSIONS Experimental results show that the proposed model is capable of accurate segmentation and bias field estimation simultaneously. The proposed model suppresses the side effect of the over-smoothing object boundary. Moreover, our model has good accuracy in the segmentation of images with extreme intensity non-uniformity.
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