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Al-Kadi OS, Al-Emaryeen R, Al-Nahhas S, Almallahi I, Braik R, Mahafza W. Empowering brain cancer diagnosis: harnessing artificial intelligence for advanced imaging insights. Rev Neurosci 2024; 35:399-419. [PMID: 38291768 DOI: 10.1515/revneuro-2023-0115] [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: 09/19/2023] [Accepted: 12/10/2023] [Indexed: 02/01/2024]
Abstract
Artificial intelligence (AI) is increasingly being used in the medical field, specifically for brain cancer imaging. In this review, we explore how AI-powered medical imaging can impact the diagnosis, prognosis, and treatment of brain cancer. We discuss various AI techniques, including deep learning and causality learning, and their relevance. Additionally, we examine current applications that provide practical solutions for detecting, classifying, segmenting, and registering brain tumors. Although challenges such as data quality, availability, interpretability, transparency, and ethics persist, we emphasise the enormous potential of intelligent applications in standardising procedures and enhancing personalised treatment, leading to improved patient outcomes. Innovative AI solutions have the power to revolutionise neuro-oncology by enhancing the quality of routine clinical practice.
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Affiliation(s)
- Omar S Al-Kadi
- King Abdullah II School for Information Technology, University of Jordan, Amman, 11942, Jordan
| | - Roa'a Al-Emaryeen
- King Abdullah II School for Information Technology, University of Jordan, Amman, 11942, Jordan
| | - Sara Al-Nahhas
- King Abdullah II School for Information Technology, University of Jordan, Amman, 11942, Jordan
| | - Isra'a Almallahi
- Department of Diagnostic Radiology, Jordan University Hospital, Amman, 11942, Jordan
| | - Ruba Braik
- Department of Diagnostic Radiology, Jordan University Hospital, Amman, 11942, Jordan
| | - Waleed Mahafza
- Department of Diagnostic Radiology, Jordan University Hospital, Amman, 11942, Jordan
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2
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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.
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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
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Ghamry FM, El-Shafai W, El-Hag NA, El-Banby GM, El-Fishawy AS, Khalaf AAM, El-Samie FEA, Soliman NF, Dessouky MI. An improved hybrid framework for brain tumor detection. JOURNAL OF OPTICS 2023. [DOI: 10.1007/s12596-023-01114-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/26/2023] [Indexed: 09/02/2023]
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Popat M, Patel S. Research perspective and review towards brain tumour segmentation and classification using different image modalities. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2124546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Mayuri Popat
- U & P.U. Patel Department of Computer Engineering, Chandubhai S Patel Institute of Technology (CSPIT), Charotar University of Science and Technology (CHARUSAT), Gujarat, India
| | - Sanskruti Patel
- Smt. Chandaben Mohanbhai Patel Institute of Computer Applications (CMPICA), Charotar University of Science and Technology (CHARUSAT), Gujarat, India
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Kataria P, Dogra A, Sharma T, Goyal B. Trends in DNN Model Based Classification and Segmentation of Brain Tumor Detection. Open Neuroimag J 2022. [DOI: 10.2174/18744400-v15-e2206290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background:
Due to the complexities of scrutinizing and diagnosing brain tumors from MR images, brain tumor analysis has become one of the most indispensable concerns. Characterization of a brain tumor before any treatment, such as radiotherapy, requires decisive treatment planning and accurate implementation. As a result, early detection of brain tumors is imperative for better clinical outcomes and subsequent patient survival.
Introduction:
Brain tumor segmentation is a crucial task in medical image analysis. Because of tumor heterogeneity and varied intensity patterns, manual segmentation takes a long time, limiting the use of accurate quantitative interventions in clinical practice. Automated computer-based brain tumor image processing has become more valuable with technological advancement. With various imaging and statistical analysis tools, deep learning algorithms offer a viable option to enable health care practitioners to rule out the disease and estimate the growth.
Methods:
This article presents a comprehensive evaluation of conventional machine learning models as well as evolving deep learning techniques for brain tumor segmentation and classification.
Conclusion:
In this manuscript, a hierarchical review has been presented for brain tumor segmentation and detection. It is found that the segmentation methods hold a wide margin of improvement in the context of the implementation of adaptive thresholding and segmentation methods, the feature training and mapping requires redundancy correction, the input data training needs to be more exhaustive and the detection algorithms are required to be robust in terms of handling online input data analysis/tumor detection.
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Das S, Nayak GK, Saba L, Kalra M, Suri JS, Saxena S. An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review. Comput Biol Med 2022; 143:105273. [PMID: 35228172 DOI: 10.1016/j.compbiomed.2022.105273] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/15/2022] [Accepted: 01/24/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has become a prominent technique for medical diagnosis and represents an essential role in detecting brain tumors. Although AI-based models are widely used in brain lesion segmentation (BLS), understanding their effectiveness is challenging due to their complexity and diversity. Several reviews on brain tumor segmentation are available, but none of them describe a link between the threats due to risk-of-bias (RoB) in AI and its architectures. In our review, we focused on linking RoB and different AI-based architectural Cluster in popular DL framework. Further, due to variance in these designs and input data types in medical imaging, it is necessary to present a narrative review considering all facets of BLS. APPROACH The proposed study uses a PRISMA strategy based on 75 relevant studies found by searching PubMed, Scopus, and Google Scholar. Based on the architectural evolution, DL studies were subsequently categorized into four classes: convolutional neural network (CNN)-based, encoder-decoder (ED)-based, transfer learning (TL)-based, and hybrid DL (HDL)-based architectures. These studies were then analyzed considering 32 AI attributes, with clusters including AI architecture, imaging modalities, hyper-parameters, performance evaluation metrics, and clinical evaluation. Then, after these studies were scored for all attributes, a composite score was computed, normalized, and ranked. Thereafter, a bias cutoff (AP(ai)Bias 1.0, AtheroPoint, Roseville, CA, USA) was established to detect low-, moderate- and high-bias studies. CONCLUSION The four classes of architectures, from best-to worst-performing, are TL > ED > CNN > HDL. ED-based models had the lowest AI bias for BLS. This study presents a set of three primary and six secondary recommendations for lowering the RoB.
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Affiliation(s)
- Suchismita Das
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India; CSE Department, KIIT Deemed to be University, Bhubaneswar, Odisha, India
| | - G K Nayak
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India
| | - Luca Saba
- Department of Radiology, AOU, University of Cagliari, Cagliari, Italy
| | - Mannudeep Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA, USA.
| | - Sanjay Saxena
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India
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Deep 3D Neural Network for Brain Structures Segmentation Using Self-Attention Modules in MRI Images. SENSORS 2022; 22:s22072559. [PMID: 35408173 PMCID: PMC9002763 DOI: 10.3390/s22072559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/15/2022] [Accepted: 03/21/2022] [Indexed: 01/03/2023]
Abstract
In recent years, the use of deep learning-based models for developing advanced healthcare systems has been growing due to the results they can achieve. However, the majority of the proposed deep learning-models largely use convolutional and pooling operations, causing a loss in valuable data and focusing on local information. In this paper, we propose a deep learning-based approach that uses global and local features which are of importance in the medical image segmentation process. In order to train the architecture, we used extracted three-dimensional (3D) blocks from the full magnetic resonance image resolution, which were sent through a set of successive convolutional neural network (CNN) layers free of pooling operations to extract local information. Later, we sent the resulting feature maps to successive layers of self-attention modules to obtain the global context, whose output was later dispatched to the decoder pipeline composed mostly of upsampling layers. The model was trained using the Mindboggle-101 dataset. The experimental results showed that the self-attention modules allow segmentation with a higher Mean Dice Score of 0.90 ± 0.036 compared with other UNet-based approaches. The average segmentation time was approximately 0.038 s per brain structure. The proposed model allows tackling the brain structure segmentation task properly. Exploiting the global context that the self-attention modules incorporate allows for more precise and faster segmentation. We segmented 37 brain structures and, to the best of our knowledge, it is the largest number of structures under a 3D approach using attention mechanisms.
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Qu Y, Li X, Yan Z, Zhao L, Zhang L, Liu C, Xie S, Li K, Metaxas D, Wu W, Hao Y, Dai K, Zhang S, Tao X, Ai S. Surgical planning of pelvic tumor using multi-view CNN with relation-context representation learning. Med Image Anal 2021; 69:101954. [PMID: 33550006 DOI: 10.1016/j.media.2020.101954] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 11/21/2020] [Accepted: 12/28/2020] [Indexed: 12/20/2022]
Abstract
Limb salvage surgery of malignant pelvic tumors is the most challenging procedure in musculoskeletal oncology due to the complex anatomy of the pelvic bones and soft tissues. It is crucial to accurately resect the pelvic tumors with appropriate margins in this procedure. However, there is still a lack of efficient and repetitive image planning methods for tumor identification and segmentation in many hospitals. In this paper, we present a novel deep learning-based method to accurately segment pelvic bone tumors in MRI. Our method uses a multi-view fusion network to extract pseudo-3D information from two scans in different directions and improves the feature representation by learning a relational context. In this way, it can fully utilize spatial information in thick MRI scans and reduce over-fitting when learning from a small dataset. Our proposed method was evaluated on two independent datasets collected from 90 and 15 patients, respectively. The segmentation accuracy of our method was superior to several comparing methods and comparable to the expert annotation, while the average time consumed decreased about 100 times from 1820.3 seconds to 19.2 seconds. In addition, we incorporate our method into an efficient workflow to improve the surgical planning process. Our workflow took only 15 minutes to complete surgical planning in a phantom study, which is a dramatic acceleration compared with the 2-day time span in a traditional workflow.
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Affiliation(s)
- Yang Qu
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Xiaomin Li
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Zhennan Yan
- SenseBrain Technology, Princeton, NJ 08540, USA
| | - Liang Zhao
- SenseTime Research, Shanghai 200233, China
| | - Lichi Zhang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030 China
| | - Chang Liu
- SenseTime Research, Shanghai 200233, China
| | | | - Kang Li
- Department of Orthopaedics, Rutgers New Jersey Medical School, Newark, NJ 07103, USA
| | - Dimitris Metaxas
- Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA
| | - Wen Wu
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Yongqiang Hao
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Kerong Dai
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; Engineering Research Center of Digital Medicine and Clinical Translation, Ministry of Education, Shanghai 200240, China
| | - Shaoting Zhang
- SenseTime Research, Shanghai 200233, China; Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
| | - Songtao Ai
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
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Zhou Z, He Z, Jia Y. AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.03.097] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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11
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TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation. SENSORS 2020; 20:s20154203. [PMID: 32731598 PMCID: PMC7435374 DOI: 10.3390/s20154203] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/22/2020] [Accepted: 07/24/2020] [Indexed: 11/16/2022]
Abstract
The high human labor demand involved in collecting paired medical imaging data severely impedes the application of deep learning methods to medical image processing tasks such as tumor segmentation. The situation is further worsened when collecting multi-modal image pairs. However, this issue can be resolved through the help of generative adversarial networks, which can be used to generate realistic images. In this work, we propose a novel framework, named TumorGAN, to generate image segmentation pairs based on unpaired adversarial training. To improve the quality of the generated images, we introduce a regional perceptual loss to enhance the performance of the discriminator. We also develop a regional L1 loss to constrain the color of the imaged brain tissue. Finally, we verify the performance of TumorGAN on a public brain tumor data set, BraTS 2017. The experimental results demonstrate that the synthetic data pairs generated by our proposed method can practically improve tumor segmentation performance when applied to segmentation network training.
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Nalepa J, Ribalta Lorenzo P, Marcinkiewicz M, Bobek-Billewicz B, Wawrzyniak P, Walczak M, Kawulok M, Dudzik W, Kotowski K, Burda I, Machura B, Mrukwa G, Ulrych P, Hayball MP. Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors. Artif Intell Med 2020; 102:101769. [DOI: 10.1016/j.artmed.2019.101769] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 10/28/2019] [Accepted: 11/20/2019] [Indexed: 02/01/2023]
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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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A Multi-class Image Classifier for Assisting in Tumor Detection of Brain Using Deep Convolutional Neural Network. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-981-13-8969-6_6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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Ribalta Lorenzo P, Nalepa J, Bobek-Billewicz B, Wawrzyniak P, Mrukwa G, Kawulok M, Ulrych P, Hayball MP. Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 176:135-148. [PMID: 31200901 DOI: 10.1016/j.cmpb.2019.05.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 04/05/2019] [Accepted: 05/10/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Magnetic resonance imaging (MRI) is an indispensable tool in diagnosing brain-tumor patients. Automated tumor segmentation is being widely researched to accelerate the MRI analysis and allow clinicians to precisely plan treatment-accurate delineation of brain tumors is a critical step in assessing their volume, shape, boundaries, and other characteristics. However, it is still a very challenging task due to inherent MR data characteristics and high variability, e.g., in tumor sizes or shapes. We present a new deep learning approach for accurate brain tumor segmentation which can be trained from small and heterogeneous datasets annotated by a human reader (providing high-quality ground-truth segmentation is very costly in practice). METHODS In this paper, we present a new deep learning technique for segmenting brain tumors from fluid attenuation inversion recovery MRI. Our technique exploits fully convolutional neural networks, and it is equipped with a battery of augmentation techniques that make the algorithm robust against low data quality, and heterogeneity of small training sets. We train our models using only positive (tumorous) examples, due to the limited amount of available data. RESULTS Our algorithm was tested on a set of stage II-IV brain-tumor patients (image data collected using MAGNETOM Prisma 3T, Siemens). Rigorous experiments, backed up with statistical tests, revealed that our approach outperforms the state-of-the-art approach (utilizing hand-crafted features) in terms of segmentation accuracy, offers very fast training and instant segmentation (analysis of an image takes less than a second). Building our deep model is 1.3 times faster compared with extracting features for extremely randomized trees, and this training time can be controlled. Finally, we showed that too aggressive data augmentation may lead to deteriorated performance of the model, especially in the fixed-budget training (with maximum numbers of training epochs). CONCLUSIONS Our method yields the better performance when compared with the state of the art method which utilizes hand-crafted features. In addition, our deep network can be effectively applied to difficult (small, imbalanced, and heterogeneous) datasets, offers controllable training time, and infers in real-time.
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Affiliation(s)
| | - Jakub Nalepa
- Future Processing, Bojkowska 37A, 44-100 Gliwice, Poland; Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.
| | - Barbara Bobek-Billewicz
- Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Wybrzeze Armii Krajowej 15, 44-102 Gliwice, Poland.
| | - Pawel Wawrzyniak
- Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Wybrzeze Armii Krajowej 15, 44-102 Gliwice, Poland.
| | | | - Michal Kawulok
- Future Processing, Bojkowska 37A, 44-100 Gliwice, Poland; Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.
| | - Pawel Ulrych
- Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Wybrzeze Armii Krajowej 15, 44-102 Gliwice, Poland.
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Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S. Entropy based segmentation of tumor from brain MR images – a study with teaching learning based optimization. Pattern Recognit Lett 2017. [DOI: 10.1016/j.patrec.2017.05.028] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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17
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Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE 2017. [DOI: 10.1007/978-3-319-60964-5_44] [Citation(s) in RCA: 312] [Impact Index Per Article: 44.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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18
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Yilmaz B, Durdu A, Emlik GD. A new method for skull stripping in brain MRI using multistable cellular neural networks. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2834-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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19
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Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Assist Radiol Surg 2016; 12:183-203. [PMID: 27651330 PMCID: PMC5263212 DOI: 10.1007/s11548-016-1483-3] [Citation(s) in RCA: 171] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 08/31/2016] [Indexed: 12/03/2022]
Abstract
Purpose We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). Methods The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. Results The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. Conclusions This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.
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Affiliation(s)
- Mohammadreza Soltaninejad
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK.
| | - Guang Yang
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, SW17 0RE, UK.,National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Tryphon Lambrou
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Nigel Allinson
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Timothy L Jones
- Atkinson Morley Department of Neurosurgery, St George's Hospital London, London, SW17 0RE, UK
| | - Thomas R Barrick
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, SW17 0RE, UK
| | - Franklyn A Howe
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, SW17 0RE, UK
| | - Xujiong Ye
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK
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Vorontsov E, Tang A, Roy D, Pal CJ, Kadoury S. Metastatic liver tumour segmentation with a neural network-guided 3D deformable model. Med Biol Eng Comput 2016; 55:127-139. [DOI: 10.1007/s11517-016-1495-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 03/24/2016] [Indexed: 01/08/2023]
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Koley S, Sadhu AK, Mitra P, Chakraborty B, Chakraborty C. Delineation and diagnosis of brain tumors from post contrast T1-weighted MR images using rough granular computing and random forest. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.01.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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ASSIA CHERFA, YAZID CHERFA, SAID MOUDACHE. SEGMENTATION OF BRAIN MRIs BY SUPPORT VECTOR MACHINE: DETECTION AND CHARACTERIZATION OF STROKES. J MECH MED BIOL 2015. [DOI: 10.1142/s0219519415500761] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The aim of our work is the segmentation of healthy and pathological brains to obtain brain structures and extract strokes. We used real magnetic resonance (MR) images weighted on diffusion. The brain was isolated, and the images were filtered by an anisotropic filter, and then segmented by support vector machines (SVMs). We first applied the method on synthetic images to test the performance of the algorithm and adjust the parameters. Then, we compared our results with those obtained by a cooperative approach proposed in a previous paper.
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Affiliation(s)
- CHERFA ASSIA
- Department of Electronics, Technology Faculty, University of Blida 09000, Algeria
| | - CHERFA YAZID
- Department of Electronics, Technology Faculty, University of Blida 09000, Algeria
| | - MOUDACHE SAID
- Department of Electronics, Technology Faculty, University of Blida 09000, Algeria
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Vorontsov E, Abi-Jaoudeh N, Kadoury S. Metastatic Liver Tumor Segmentation Using Texture-Based Omni-Directional Deformable Surface Models. LECTURE NOTES IN COMPUTER SCIENCE 2014. [DOI: 10.1007/978-3-319-13692-9_7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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