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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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Abd-Ellah MK, Awad AI, Khalaf AAM, Ibraheem AM. Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks. Sci Rep 2024; 14:9501. [PMID: 38664436 PMCID: PMC11045751 DOI: 10.1038/s41598-024-59566-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.
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Affiliation(s)
| | - Ali Ismail Awad
- College of Information Technology, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates.
- Faculty of Engineering, Al-Azhar University, P.O. Box 83513, Qena, Egypt.
| | - Ashraf A M Khalaf
- Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61519, Egypt
| | - Amira Mofreh Ibraheem
- Faculty of Artificial Intelligence, Egyptian Russian University, Cairo, 11829, Egypt
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Reza SMS, Islam A, Iftekharuddin KM. Texture Estimation for Abnormal Tissue Segmentation in Brain MRI. ADVANCES IN NEUROBIOLOGY 2024; 36:469-486. [PMID: 38468048 DOI: 10.1007/978-3-031-47606-8_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
This chapter discusses multifractal texture estimation and characterization of brain lesions (necrosis, edema, enhanced tumor, nonenhanced tumor, etc.) in magnetic resonance (MR) images. This work formulates the complex texture of tumor in MR images using a stochastic model known as multifractional Brownian motion (mBm). Mathematical derivations of the mBm model and corresponding algorithm to extract the spatially varying multifractal texture feature are discussed. Extracted multifractal texture feature is fused with other effective features to enhance the tissue characteristics. Segmentation of the tissues is performed using a feature-based classification method. The efficacy of the mBm texture feature in segmenting different abnormal tissues is demonstrated using a large-scale publicly available clinical dataset. Experimental results and performance of the methods confirm the efficacy of the proposed technique in an automatic segmentation of abnormal tissues in multimodal (T1, T2, Flair, and T1contrast) brain MRIs.
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Affiliation(s)
- Syed M S Reza
- Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA
| | - Atiq Islam
- Applied Research, Ebay Inc, San Jose, CA, USA
| | - Khan M Iftekharuddin
- Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA.
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Balaha HM, Hassan AES. A variate brain tumor segmentation, optimization, and recognition framework. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10337-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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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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Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147282] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest-performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions. Faster CNN used the VGG 16 architecture as a primary network to generate convolutional feature maps, then classified these to yield tumor region suggestions. The prediction accuracy was used to assess performance. Our suggested methodology was evaluated on a dataset for brain tumor diagnosis using MR images comprising 253 MRI brain images, with 155 showing tumors. Our approach could identify brain tumors in MR images. In the testing data, the algorithm outperformed the current conventional approaches for detecting brain tumors (Precision = 96%, 98.15%, 98.41% and F1-score = 91.78%, 92.6% and 91.29% respectively) and achieved an excellent accuracy of CNN 96%, VGG 16 98.5% and Ensemble Model 98.14%. The study also presents future recommendations regarding the proposed research work.
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Multi-scale brain tumor segmentation combined with deep supervision. Int J Comput Assist Radiol Surg 2021; 17:561-568. [PMID: 34894336 DOI: 10.1007/s11548-021-02515-w] [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: 06/22/2021] [Accepted: 09/29/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE Fully convolutional neural networks (FCNNs) have achieved good performance in the field of medical image segmentation. FCNNs that use multimodal images and multi-scale feature extraction have higher accuracy for brain tumor segmentation. Therefore, we have made some improvements to U-Net for fully automated segmentation of gliomas using multimodal images. And we named it multi-scale dilate network with deep supervision (MSD-Net). METHODS MSD-Net is a symmetrical structure composed of a down-sampling process and an up-sampling process. In the down-sampling process, we use the multi-scale feature extraction block (ME) to extract multi-scale features and focus on primary features. Unlike other methods, ME consists of dilate convolution and standard convolution. Dilate convolution extracts multi-scale informations and standard convolution merges features of different scales. Hence, the output of the ME contains local information and global information. During the up-sampling process, we add a deep supervision block (DSB), which can shorten the length of back-propagation. In this paper, we pay more attention to the importance of shallow features for feature restoration. RESULTS Our network validated in the BraTS17's validation dataset. The DSC scores of MSD-Net for complete tumor, tumor core and enhancing tumor were 0.88, 0.81 and 0.78, respectively, which outperforms most networks. CONCLUSION This study shows that ME enhances the feature extraction ability of the network and improves the accuracy of segmentation results. DSB speeds up the convergence of the network. In addition, we should also pay attention to the contribution of shallow features to feature restoration.
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Nawaz M, Nazir T, Masood M, Mehmood A, Mahum R, Khan MA, Kadry S, Thinnukool O. Analysis of Brain MRI Images Using Improved CornerNet Approach. Diagnostics (Basel) 2021; 11:diagnostics11101856. [PMID: 34679554 PMCID: PMC8535141 DOI: 10.3390/diagnostics11101856] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/24/2021] [Accepted: 09/27/2021] [Indexed: 01/18/2023] Open
Abstract
The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.
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Affiliation(s)
- Marriam Nawaz
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.N.); (T.N.); (M.M.); (A.M.); (R.M.)
| | - Tahira Nazir
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.N.); (T.N.); (M.M.); (A.M.); (R.M.)
| | - Momina Masood
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.N.); (T.N.); (M.M.); (A.M.); (R.M.)
| | - Awais Mehmood
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.N.); (T.N.); (M.M.); (A.M.); (R.M.)
| | - Rabbia Mahum
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.N.); (T.N.); (M.M.); (A.M.); (R.M.)
| | | | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway;
| | - Orawit Thinnukool
- Research Group of Embedded Systems and Mobile Application in Health Science, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
- Correspondence:
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Kang J, Ullah Z, Gwak J. MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers. SENSORS 2021; 21:s21062222. [PMID: 33810176 PMCID: PMC8004778 DOI: 10.3390/s21062222] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/13/2021] [Accepted: 03/17/2021] [Indexed: 11/21/2022]
Abstract
Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.
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Affiliation(s)
- Jaeyong Kang
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
| | - Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of IT Convergence (Brain Korea PLUS 21), Korea National University of Transportation, Chungju 27469, Korea
- Correspondence: ; Tel.: +82-43-841-5852
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Gryska E, Schneiderman J, Björkman-Burtscher I, Heckemann RA. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ Open 2021; 11:e042660. [PMID: 33514580 PMCID: PMC7849889 DOI: 10.1136/bmjopen-2020-042660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 01/09/2021] [Accepted: 01/12/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field. DESIGN Scoping review. SETTING Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison. RESULTS Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity. CONCLUSIONS The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.
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Affiliation(s)
- Emilia Gryska
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
| | - Justin Schneiderman
- Sektionen för klinisk neurovetenskap, Goteborgs Universitet Institutionen for Neurovetenskap och fysiologi, Goteborg, Sweden
| | | | - Rolf A Heckemann
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
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Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10326-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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12
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Brain Tumor Detection and Segmentation in MR Images Using Deep Learning. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019. [DOI: 10.1007/s13369-019-03967-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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ParthaSarathi M, Ansari MA. Multimodal Retrieval Framework for Brain Volumes in 3D MR Volumes. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0287-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Semiautomatic Segmentation of Glioma on Mobile Devices. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:8054939. [PMID: 29065648 PMCID: PMC5504950 DOI: 10.1155/2017/8054939] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 02/28/2017] [Accepted: 03/23/2017] [Indexed: 11/17/2022]
Abstract
Brain tumor segmentation is the first and the most critical step in clinical applications of radiomics. However, segmenting brain images by radiologists is labor intense and prone to inter- and intraobserver variability. Stable and reproducible brain image segmentation algorithms are thus important for successful tumor detection in radiomics. In this paper, we propose a supervised brain image segmentation method, especially for magnetic resonance (MR) brain images with glioma. This paper uses hard edge multiplicative intrinsic component optimization to preprocess glioma medical image on the server side, and then, the doctors could supervise the segmentation process on mobile devices in their convenient time. Since the preprocessed images have the same brightness for the same tissue voxels, they have small data size (typically 1/10 of the original image size) and simple structure of 4 types of intensity value. This observation thus allows follow-up steps to be processed on mobile devices with low bandwidth and limited computing performance. Experiments conducted on 1935 brain slices from 129 patients show that more than 30% of the sample can reach 90% similarity; over 60% of the samples can reach 85% similarity, and more than 80% of the sample could reach 75% similarity. The comparisons with other segmentation methods also demonstrate both efficiency and stability of the proposed approach.
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A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread. Bull Math Biol 2017; 80:1259-1291. [DOI: 10.1007/s11538-017-0271-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 03/15/2017] [Indexed: 02/01/2023]
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Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H. Brain tumor segmentation with Deep Neural Networks. Med Image Anal 2017; 35:18-31. [DOI: 10.1016/j.media.2016.05.004] [Citation(s) in RCA: 1717] [Impact Index Per Article: 245.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Revised: 03/02/2016] [Accepted: 05/11/2016] [Indexed: 11/28/2022]
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Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.09.051] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Wang K, Ma C. A robust statistics driven volume-scalable active contour for segmenting anatomical structures in volumetric medical images with complex conditions. Biomed Eng Online 2016; 15:39. [PMID: 27074891 PMCID: PMC4831199 DOI: 10.1186/s12938-016-0153-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Accepted: 04/01/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Accurate segmentation of anatomical structures in medical images is a critical step in the development of computer assisted intervention systems. However, complex image conditions, such as intensity inhomogeneity, noise and weak object boundary, often cause considerable difficulties in medical image segmentation. To cope with these difficulties, we propose a novel robust statistics driven volume-scalable active contour framework, to extract desired object boundary from magnetic resonance (MR) and computed tomography (CT) imagery in 3D. METHODS We define an energy functional in terms of the initial seeded labels and two fitting functions that are derived from object local robust statistics features. This energy is then incorporated into a level set scheme which drives the active contour evolving and converging at the desired position of the object boundary. Due to the local robust statistics and the volume scaling function in the energy fitting term, the object features in local volumes are learned adaptively to guide the motion of the contours, which thereby guarantees the capability of our method to cope with intensity inhomogeneity, noise and weak boundary. In addition, the initialization of active contour is simplified by select several seeds in the object and/or background to eliminate the sensitivity to initialization. RESULTS The proposed method was applied to extensive public available volumetric medical images with challenging image conditions. The segmentation results of various anatomical structures, such as white matter (WM), atrium, caudate nucleus and brain tumor, were evaluated quantitatively by comparing with the corresponding ground truths. It was found that the proposed method achieves consistent and coherent segmentation accuracy of 0.9246 ± 0.0068 for WM, 0.9043 ± 0.0131 for liver tumors, 0.8725 ± 0.0374 for caudate nucleus, 0.8802 ± 0.0595 for brain tumors, etc., measured by Dice similarity coefficients value for the overlap between the algorithm one and the ground truth. Further comparative experimental results showed desirable performances of the proposed method over several well-known segmentation methods in terms of accuracy and robustness. CONCLUSION We proposed an approach to accurate segment volumetric medical images with complex conditions. The accuracy of segmentation, robustness to noise and contour initialization were validated on the basis of extensive MR and CT volumes.
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Affiliation(s)
- Kuanquan Wang
- School of Computer Science and Technology, Biocomputing Research Center, Harbin Institute of Technology, Harbin, China.
| | - Chao Ma
- School of Computer Science and Technology, Biocomputing Research Center, Harbin Institute of Technology, Harbin, China
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21
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Guo D, Fridriksson J, Fillmore P, Rorden C, Yu H, Zheng K, Wang S. Automated lesion detection on MRI scans using combined unsupervised and supervised methods. BMC Med Imaging 2015; 15:50. [PMID: 26518734 PMCID: PMC4628334 DOI: 10.1186/s12880-015-0092-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 10/16/2015] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods. METHODS First, unsupervised methods perform a unified segmentation normalization to warp images from the native space into a standard space and to generate probability maps for different tissue types, e.g., gray matter, white matter and fluid. This allows us to construct an initial lesion probability map by comparing the normalized MRI to healthy control subjects. Then, we perform non-rigid and reversible atlas-based registration to refine the probability maps of gray matter, white matter, external CSF, ventricle, and lesions. These probability maps are combined with the normalized MRI to construct three types of features, with which we use supervised methods to train three support vector machine (SVM) classifiers for a combined classifier. Finally, the combined classifier is used to accomplish lesion detection. RESULTS We tested this method using T1-weighted MRIs from 60 in-house stroke patients. Using leave-one-out cross validation, the proposed method can achieve an average Dice coefficient of 73.1% when compared to lesion maps hand-delineated by trained neurologists. Furthermore, we tested the proposed method on the T1-weighted MRIs in the MICCAI BRATS 2012 dataset. The proposed method can achieve an average Dice coefficient of 66.5% in comparison to the expert annotated tumor maps provided in MICCAI BRATS 2012 dataset. In addition, on these two test datasets, the proposed method shows competitive performance to three state-of-the-art methods, including Stamatakis et al., Seghier et al., and Sanjuan et al. CONCLUSIONS In this paper, we introduced a novel automated procedure for lesion detection from T1-weighted MRIs by combining both an unsupervised and a supervised component. In the unsupervised component, we proposed a method to identify lesioned hemisphere to help normalize the patient MRI with lesions and initialize/refine a lesion probability map. In the supervised component, we extracted three different-order statistical features from both the tissue/lesion probability maps obtained from the unsupervised component and the original MRI intensity. Three support vector machine classifiers are then trained for the three features respectively and combined for final voxel-based lesion classification.
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Affiliation(s)
- Dazhou Guo
- Department of Computer Science & Engineering, University of South Carolina, 301 Main Street, Columbia, 29201, USA.
| | - Julius Fridriksson
- Department of Communication Science & Disorders, University of South Carolina, 915 Greene Street, Columbia, 29208, USA.
| | - Paul Fillmore
- Department of Communication Science & Disorders, University of South Carolina, 915 Greene Street, Columbia, 29208, USA.
| | - Christopher Rorden
- Department of Communication Science & Disorders, University of South Carolina, 915 Greene Street, Columbia, 29208, USA.
| | - Hongkai Yu
- Department of Computer Science & Engineering, University of South Carolina, 301 Main Street, Columbia, 29201, USA.
| | - Kang Zheng
- Department of Computer Science & Engineering, University of South Carolina, 301 Main Street, Columbia, 29201, USA.
| | - Song Wang
- Department of Computer Science & Engineering, University of South Carolina, 301 Main Street, Columbia, 29201, USA.
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3D multimodal MRI brain glioma tumor and edema segmentation: a graph cut distribution matching approach. Comput Med Imaging Graph 2014; 40:108-19. [PMID: 25467804 DOI: 10.1016/j.compmedimag.2014.10.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Revised: 08/15/2014] [Accepted: 10/16/2014] [Indexed: 11/20/2022]
Abstract
This study investigates a fast distribution-matching, data-driven algorithm for 3D multimodal MRI brain glioma tumor and edema segmentation in different modalities. We learn non-parametric model distributions which characterize the normal regions in the current data. Then, we state our segmentation problems as the optimization of several cost functions of the same form, each containing two terms: (i) a distribution matching prior, which evaluates a global similarity between distributions, and (ii) a smoothness prior to avoid the occurrence of small, isolated regions in the solution. Obtained following recent bound-relaxation results, the optima of the cost functions yield the complement of the tumor region or edema region in nearly real-time. Based on global rather than pixel wise information, the proposed algorithm does not require an external learning from a large, manually-segmented training set, as is the case of the existing methods. Therefore, the ensuing results are independent of the choice of a training set. Quantitative evaluations over the publicly available training and testing data set from the MICCAI multimodal brain tumor segmentation challenge (BraTS 2012) demonstrated that our algorithm yields a highly competitive performance for complete edema and tumor segmentation, among nine existing competing methods, with an interesting computing execution time (less than 0.5s per image).
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Computer-extracted MR imaging features are associated with survival in glioblastoma patients. J Neurooncol 2014; 120:483-8. [PMID: 25151504 DOI: 10.1007/s11060-014-1580-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2014] [Accepted: 08/05/2014] [Indexed: 10/24/2022]
Abstract
Automatic survival prognosis in glioblastoma (GBM) could result in improved treatment planning for the patient. The purpose of this research is to investigate the association of survival in GBM patients with tumor features in pre-operative magnetic resonance (MR) images assessed using a fully automatic computer algorithm. MR imaging data for 68 patients from two US institutions were used in this study. The images were obtained from the Cancer Imaging Archive. A fully automatic computer vision algorithm was applied to segment the images and extract eight imaging features from the MRI studies. The features included tumor side, proportion of enhancing tumor, proportion of necrosis, T1/FLAIR ratio, major axis length, minor axis length, tumor volume, and thickness of enhancing margin. We constructed a multivariate Cox proportional hazards regression model and used a likelihood ratio test to establish whether the imaging features are prognostic of survival. We also evaluated the individual prognostic value of each feature through multivariate analysis using the multivariate Cox model and univariate analysis using univariate Cox models for each feature. We found that the automatically extracted imaging features were predictive of survival (p = 0.031). Multivariate analysis of individual features showed that two individual features were predictive of survival: proportion of enhancing tumor (p = 0.013), and major axis length (p = 0.026). Univariate analysis indicated the same two features as significant (p = 0.021, and p = 0.017 respectively). We conclude that computer-extracted MR imaging features can be used for survival prognosis in GBM patients.
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Zhang J, Barboriak DP, Hobbs H, Mazurowski MA. A fully automatic extraction of magnetic resonance image features in glioblastoma patients. Med Phys 2014; 41:042301. [DOI: 10.1118/1.4866218] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Galinsky VL, Frank LR. Automated segmentation and shape characterization of volumetric data. Neuroimage 2014; 92:156-68. [PMID: 24521852 DOI: 10.1016/j.neuroimage.2014.01.053] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Revised: 12/19/2013] [Accepted: 01/28/2014] [Indexed: 10/25/2022] Open
Abstract
Characterization of complex shapes embedded within volumetric data is an important step in a wide range of applications. Standard approaches to this problem employ surface-based methods that require inefficient, time consuming, and error prone steps of surface segmentation and inflation to satisfy the uniqueness or stability of subsequent surface fitting algorithms. Here we present a novel method based on a spherical wave decomposition (SWD) of the data that overcomes several of these limitations by directly analyzing the entire data volume, obviating the segmentation, inflation, and surface fitting steps, significantly reducing the computational time and eliminating topological errors while providing a more detailed quantitative description based upon a more complete theoretical framework of volumetric data. The method is demonstrated and compared to the current state-of-the-art neuroimaging methods for segmentation and characterization of volumetric magnetic resonance imaging data of the human brain.
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Affiliation(s)
- Vitaly L Galinsky
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92093-0854, USA; Electrical and Computer Engineering Department, University of California at San Diego, La Jolla, CA 92093-0407, USA.
| | - Lawrence R Frank
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92093-0854, USA; Center for Functional MRI, University of California at San Diego, La Jolla, CA 92093-0677, USA.
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Sanjuán A, Price CJ, Mancini L, Josse G, Grogan A, Yamamoto AK, Geva S, Leff AP, Yousry TA, Seghier ML. Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors. Front Neurosci 2013; 7:241. [PMID: 24381535 PMCID: PMC3865426 DOI: 10.3389/fnins.2013.00241] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 11/27/2013] [Indexed: 11/20/2022] Open
Abstract
Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit “extra prior” for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic.
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Affiliation(s)
- Ana Sanjuán
- Wellcome Trust Centre for Neuroimaging, University College of London London, UK ; Departamento de Psicología Básica, Clínica y Psicobiología, Universitat Jaume I Castellón, Spain
| | - Cathy J Price
- Wellcome Trust Centre for Neuroimaging, University College of London London, UK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery London, UK
| | - Goulven Josse
- Hôpital de la Pitié-Salpêtrière, Institut du Cerveau et de la Moëlle épinière Paris, France
| | - Alice Grogan
- Wellcome Trust Centre for Neuroimaging, University College of London London, UK
| | - Adam K Yamamoto
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery London, UK
| | - Sharon Geva
- Developmental Cognitive Neuroscience Unit, Institute of Child Health, University College of London London, UK
| | - Alex P Leff
- Wellcome Trust Centre for Neuroimaging, University College of London London, UK ; Institute of Cognitive Neuroscience, University College of London London, UK
| | - Tarek A Yousry
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery London, UK
| | - Mohamed L Seghier
- Wellcome Trust Centre for Neuroimaging, University College of London London, UK
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Jiang J, Wu Y, Huang M, Yang W, Chen W, Feng Q. 3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets. Comput Med Imaging Graph 2013; 37:512-21. [DOI: 10.1016/j.compmedimag.2013.05.007] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2013] [Revised: 05/28/2013] [Accepted: 05/31/2013] [Indexed: 11/24/2022]
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Bauer S, Wiest R, Nolte LP, Reyes M. A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol 2013; 58:R97-129. [PMID: 23743802 DOI: 10.1088/0031-9155/58/13/r97] [Citation(s) in RCA: 301] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.
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Affiliation(s)
- Stefan Bauer
- Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland.
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Zikic D, Glocker B, Konukoglu E, Criminisi A, Demiralp C, Shotton J, Thomas OM, Das T, Jena R, Price SJ. Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2012; 15:369-76. [PMID: 23286152 DOI: 10.1007/978-3-642-33454-2_46] [Citation(s) in RCA: 134] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
We present a method for automatic segmentation of high-grade gliomas and their subregions from multi-channel MR images. Besides segmenting the gross tumor, we also differentiate between active cells, necrotic core, and edema. Our discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest. Our method classifies the individual tissue types simultaneously, which has the potential to simplify the classification task. The approach is computationally efficient and of low model complexity. The validation is performed on a labeled database of 40 multi-channel MR images, including DTI. We assess the effects of using DTI, and varying the amount of training data. Our segmentation results are highly accurate, and compare favorably to the state of the art.
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