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Praveenkumar S, Kalaiselvi T, Somasundaram K. Methods of Brain Extraction from Magnetic Resonance Images of Human Head: A Review. Crit Rev Biomed Eng 2023; 51:1-40. [PMID: 37581349 DOI: 10.1615/critrevbiomedeng.2023047606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Medical images are providing vital information to aid physicians in diagnosing a disease afflicting the organ of a human body. Magnetic resonance imaging is an important imaging modality in capturing the soft tissues of the brain. Segmenting and extracting the brain is essential in studying the structure and pathological condition of brain. There are several methods that are developed for this purpose. Researchers in brain extraction or segmentation need to know the current status of the work that have been done. Such an information is also important for improving the existing method to get more accurate results or to reduce the complexity of the algorithm. In this paper we review the classical methods and convolutional neural network-based deep learning brain extraction methods.
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Affiliation(s)
| | - T Kalaiselvi
- Department of Computer Science and Applications, Gandhigram Rural Institute, Gandhigram 624302, Tamil Nadu, India
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Konar D, Bhattacharyya S, Dey S, Panigrahi BK. Optimized activation for quantum-inspired self-supervised neural network based fully automated brain lesion segmentation. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03108-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Automatic brain extraction from MRI of human head scans using Helmholtz free energy principle and morphological operations. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102270] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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FDSR: A new fuzzy discriminative sparse representation method for medical image classification. Artif Intell Med 2020; 106:101876. [PMID: 32593393 DOI: 10.1016/j.artmed.2020.101876] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 05/02/2020] [Indexed: 11/22/2022]
Abstract
Recent developments in medical image analysis techniques make them essential tools in medical diagnosis. Medical imaging is always involved with different kinds of uncertainties. Managing these uncertainties has motivated extensive research on medical image classification methods, particularly for the past decade. Despite being a powerful classification tool, the sparse representation suffers from the lack of sufficient discrimination and robustness, which are required to manage the uncertainty and noisiness in medical image classification issues. It is tried to overcome this deficiency by introducing a new fuzzy discriminative robust sparse representation classifier, which benefits from the fuzzy terms in its optimization function of the dictionary learning process. In this work, we present a new medical image classification approach, fuzzy discriminative sparse representation (FDSR). The proposed fuzzy terms increase the inter-class representation difference and the intra-class representation similarity. Also, an adaptive fuzzy dictionary learning approach is used to learn dictionary atoms. FDSR is applied on Magnetic Resonance Images (MRI) from three medical image databases. The comprehensive experimental results clearly show that our approach outperforms its series of rival techniques in terms of accuracy, sensitivity, specificity, and convergence speed.
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Toward Effective Medical Image Analysis Using Hybrid Approaches—Review, Challenges and Applications. INFORMATION 2020. [DOI: 10.3390/info11030155] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Accurate medical images analysis plays a vital role for several clinical applications. Nevertheless, the immense and complex data volume to be processed make difficult the design of effective algorithms. The first aim of this paper is to examine this area of research and to provide some relevant reference sources related to the context of medical image analysis. Then, an effective hybrid solution to further improve the expected results is proposed here. It allows to consider the benefits of the cooperation of different complementary approaches such as statistical-based, variational-based and atlas-based techniques and to reduce their drawbacks. In particular, a pipeline framework that involves different steps such as a preprocessing step, a classification step and a refinement step with variational-based method is developed to identify accurately pathological regions in biomedical images. The preprocessing step has the role to remove noise and improve the quality of the images. Then the classification is based on both symmetry axis detection step and non linear learning with SVM algorithm. Finally, a level set-based model is performed to refine the boundary detection of the region of interest. In this work we will show that an accurate initialization step could enhance final performances. Some obtained results are exposed which are related to the challenging application of brain tumor segmentation.
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Marsault P, Ducassou S, Menut F, Bessou P, Havez-Enjolras M, Chateil JF. Diagnostic performance of an unenhanced MRI exam for tumor follow-up of the optic pathway gliomas in children. Neuroradiology 2019; 61:711-720. [DOI: 10.1007/s00234-019-02198-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 03/07/2019] [Indexed: 12/15/2022]
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Machine learning based brain tumour segmentation on limited data using local texture and abnormality. Comput Biol Med 2018; 98:39-47. [PMID: 29763764 DOI: 10.1016/j.compbiomed.2018.05.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 05/02/2018] [Accepted: 05/02/2018] [Indexed: 11/23/2022]
Abstract
Brain tumour segmentation in medical images is a very challenging task due to the large variety in tumour shape, position, appearance, scanning modalities and scanning parameters. Most existing segmentation algorithms use information from four different MRI-sequences, but since this is often not available, there is need for a method able to delineate the different tumour tissues based on a minimal amount of data. We present a novel approach using a Random Forests model combining voxelwise texture and abnormality features on a contrast-enhanced T1 and FLAIR MRI. We transform the two scans into 275 feature maps. A random forest model next calculates the probability to belong to 4 tumour classes or 5 normal classes. Afterwards, a dedicated voxel clustering algorithm provides the final tumour segmentation. We trained our method on the BraTS 2013 database and validated it on the larger BraTS 2017 dataset. We achieve median Dice scores of 40.9% (low-grade glioma) and 75.0% (high-grade glioma) to delineate the active tumour, and 68.4%/80.1% for the total abnormal region including edema. Our fully automated brain tumour segmentation algorithm is able to delineate contrast enhancing tissue and oedema with high accuracy based only on post-contrast T1-weighted and FLAIR MRI, whereas for non-enhancing tumour tissue and necrosis only moderate results are obtained. This makes the method especially suitable for high-grade glioma.
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Steinberg ME, Oh SC, Khoury V, Udupa JK, Steinberg DR. Lesion size measurement in femoral head necrosis. INTERNATIONAL ORTHOPAEDICS 2018; 42:1585-1591. [PMID: 29691613 DOI: 10.1007/s00264-018-3912-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 03/16/2018] [Indexed: 11/29/2022]
Abstract
BACKGROUND Management of patients with early stages of osteonecrosis of the femoral head remains controversial. Uniform use of an effective method of evaluation and classification, including both stage and lesion size, would allow for comparison and would significantly improve treatment of patients. There is no consensus on how best to determine lesion size. The purpose of this study was to evaluate and compare accuracy and ease of use of different techniques for determining the size of femoral head lesions. METHODS Twenty-five hips with stages I or II osteonecrosis were evaluated with radiographs and MRI. 3-D MRI measurements of lesion size were used as the standard against which to compare visual estimates and angular measurements: necrotic angle of Kerboul, index of necrosis, and adjusted index of necrosis. RESULTS 3-D measurements (necrotic volume) showed regular progression from 2.2 to 59.2% of the femoral head. There was a rough correlation with angular measurements; index of necrosis was closer than the necrotic angle. Visual estimates from serial MRI images were as accurate as angular measurements. CONCLUSIONS Simple visual estimates of lesion size from serial MRI images are reasonably accurate and are satisfactory for clinical use. Angular measurements provide some indication of prognosis and treatment; however, they have limited accuracy, with considerable variability between techniques. 3-D MRI volumetric measurements are the most accurate. Using current techniques and software, they are easier to use, requiring similar time and effort to angular measurements. They should be considered for clinical research and publications when the most accurate measurements are required.
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Affiliation(s)
- Marvin E Steinberg
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, 3737 Market Street, Suite 600, Philadelphia, PA, 19104, USA
| | - Seong C Oh
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Viviane Khoury
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jayaram K Udupa
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David R Steinberg
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, 3737 Market Street, Suite 600, Philadelphia, PA, 19104, USA.
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Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app8010027] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2017; 41:41-58. [PMID: 29238919 DOI: 10.1007/s13246-017-0609-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 12/06/2017] [Indexed: 10/18/2022]
Abstract
In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague boundaries and poor contrast. This novel technique takes into account both the image histogram and the uncertainty information for the computation of multiple thresholds. The benefit of the methodology is that it provides fast and improved segmentation for the complex tumorous images with imprecise gray levels. To further boost the computational speed, the mutation based particle swarm optimization is used that selects the most optimal threshold combination. The accuracy of the proposed segmentation approach has been validated on simulated, real low-grade glioma tumor volumes taken from MICCAI brain tumor segmentation (BRATS) challenge 2012 dataset and the clinical tumor images, so as to corroborate its generality and novelty. The designed technique achieves an average Dice overlap equal to 0.82010, 0.78610 and 0.94170 for three datasets. Further, a comparative analysis has also been made between the eight existing multilevel thresholding implementations so as to show the superiority of the designed technique. In comparison, the results indicate a mean improvement in Dice by an amount equal to 4.00% (p < 0.005), 9.60% (p < 0.005) and 3.58% (p < 0.005), respectively in contrast to the fuzzy tsallis approach.
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Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:8536206. [PMID: 29158887 PMCID: PMC5660817 DOI: 10.1155/2017/8536206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 07/31/2017] [Indexed: 12/02/2022]
Abstract
We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient's response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.
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Chen YT. A novel approach to segmentation and measurement of medical image using level set methods. Magn Reson Imaging 2017; 39:175-193. [PMID: 28219649 DOI: 10.1016/j.mri.2017.02.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Revised: 01/10/2017] [Accepted: 02/16/2017] [Indexed: 11/16/2022]
Abstract
The study proposes a novel approach for segmentation and visualization plus value-added surface area and volume measurements for brain medical image analysis. The proposed method contains edge detection and Bayesian based level set segmentation, surface and volume rendering, and surface area and volume measurements for 3D objects of interest (i.e., brain tumor, brain tissue, or whole brain). Two extensions based on edge detection and Bayesian level set are first used to segment 3D objects. Ray casting and a modified marching cubes algorithm are then adopted to facilitate volume and surface visualization of medical-image dataset. To provide physicians with more useful information for diagnosis, the surface area and volume of an examined 3D object are calculated by the techniques of linear algebra and surface integration. Experiment results are finally reported in terms of 3D object extraction, surface and volume rendering, and surface area and volume measurements for medical image analysis.
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Affiliation(s)
- Yao-Tien Chen
- Department of Applied Mobile Technology, Yuanpei University of Medical Technology, No. 306, Yuanpei St., HsinChu City 30015, Taiwan.
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Kaur T, Saini BS, Gupta S. A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2751-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Optimized Multi Threshold Brain Tumor Image Segmentation Using Two Dimensional Minimum Cross Entropy Based on Co-occurrence Matrix. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/978-3-319-33793-7_20] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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Wang L, Li D, Huang S. An improved parallel fuzzy connected image segmentation method based on CUDA. Biomed Eng Online 2016; 15:56. [PMID: 27175785 PMCID: PMC4866034 DOI: 10.1186/s12938-016-0165-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 04/26/2016] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Fuzzy connectedness method (FC) is an effective method for extracting fuzzy objects from medical images. However, when FC is applied to large medical image datasets, its running time will be greatly expensive. Therefore, a parallel CUDA version of FC (CUDA-kFOE) was proposed by Ying et al. to accelerate the original FC. Unfortunately, CUDA-kFOE does not consider the edges between GPU blocks, which causes miscalculation of edge points. In this paper, an improved algorithm is proposed by adding a correction step on the edge points. The improved algorithm can greatly enhance the calculation accuracy. METHODS In the improved method, an iterative manner is applied. In the first iteration, the affinity computation strategy is changed and a look up table is employed for memory reduction. In the second iteration, the error voxels because of asynchronism are updated again. RESULTS Three different CT sequences of hepatic vascular with different sizes were used in the experiments with three different seeds. NVIDIA Tesla C2075 is used to evaluate our improved method over these three data sets. Experimental results show that the improved algorithm can achieve a faster segmentation compared to the CPU version and higher accuracy than CUDA-kFOE. CONCLUSIONS The calculation results were consistent with the CPU version, which demonstrates that it corrects the edge point calculation error of the original CUDA-kFOE. The proposed method has a comparable time cost and has less errors compared to the original CUDA-kFOE as demonstrated in the experimental results. In the future, we will focus on automatic acquisition method and automatic processing.
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Affiliation(s)
- Liansheng Wang
- Department of Computer Science, School of Information Science and Engineering, Xiamen University, Xiamen, China
| | - Dong Li
- Department of Computer Science, School of Information Science and Engineering, Xiamen University, Xiamen, China
| | - Shaohui Huang
- Department of Computer Science, School of Information Science and Engineering, Xiamen University, Xiamen, China.
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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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Simi V, Joseph J. Segmentation of Glioblastoma Multiforme from MR Images – A comprehensive review. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2015. [DOI: 10.1016/j.ejrnm.2015.08.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Lambron J, Rakotonjanahary J, Loisel D, Frampas E, De Carli E, Delion M, Rialland X, Toulgoat F. Can we improve accuracy and reliability of MRI interpretation in children with optic pathway glioma? Proposal for a reproducible imaging classification. Neuroradiology 2015; 58:197-208. [PMID: 26518314 DOI: 10.1007/s00234-015-1612-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 10/20/2015] [Indexed: 10/22/2022]
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Automatic segmentation and quantitative analysis of white matter hyperintensities on FLAIR images using trimmed-likelihood estimator. Acad Radiol 2014; 21:1512-23. [PMID: 25176451 DOI: 10.1016/j.acra.2014.07.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Revised: 07/04/2014] [Accepted: 07/07/2014] [Indexed: 11/23/2022]
Abstract
RATIONALE AND OBJECTIVES Quantitative analysis of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images provides information for disease tracking, therapeutic efficacy assessment, and cognitive science research. This study developed an automatic segmentation method to detect and quantify WMHs on FLAIR images. This study aims to assess the accuracy and reproducibility of the proposed method. MATERIALS AND METHODS The FLAIR images of 82 patients (58-84 years) with different WMH burdens were acquired with the same 3T scanner (Intera-achieva SMI-2.1; Philip Medical System, Sixth Affiliated People's Hospital, Shanghai, China). The FLAIR images were preprocessed through brain extraction and intensity inhomogeneity correction. An anatomy atlas built from a set of 20 patients with different WMH burdens (mild, 11 patients; moderate, 6 patients; and severe, 3 patients) was used to estimate a control parameter in the framework of segmentation. The general flow for WMH segmentation included classification of foreground and background regions, detection of abnormally high signals, and WMH refinement. The performance of automatic segmentation was evaluated by a volumetric comparison with manual segmentation on patients with different WMH burdens. RESULTS Similarity index values for the accuracy of automatic segmentation compared to manual segmentation were consistently high for patients with different WMH burdens (mild, 0.78 ± 0.08; moderate, 0.83 ± 0.06; severe, 0.84 ± 0.08; and total, 0.80 ± 0.08). Linear regression demonstrated a strong correlation between the WMH volumes measured by the two methods in all patients (r = 0.98, P = .006). Small average differences were detected between the WMH volumes obtained through manual and automatic segmentations (mild, 4.76%; moderate, 6.84%; and severe, 7.59%). The results of Bland-Altman analysis show a system bias of 0.68 cm(3) and a standard deviation of 2.10 cm(3) over the range of 2.58-53.9 cm(3). CONCLUSIONS The developed method is accurate and efficient in detecting and quantifying differently sized WMHs on FLAIR images. Automatic segmentation is a promising computer-aided diagnostic tool to study WMHs in aging and dementia in basic research and even in clinical trials.
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Hectors SJCG, Jacobs I, Strijkers GJ, Nicolay K. Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2014; 28:363-75. [PMID: 25427885 DOI: 10.1007/s10334-014-0472-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2014] [Revised: 10/26/2014] [Accepted: 10/29/2014] [Indexed: 10/24/2022]
Abstract
OBJECT Contrast-enhanced T1-weighted imaging is usually included in MRI procedures for automatic tumor segmentation. Use of an MR contrast agent may not be appropriate for some applications, however. We assessed the feasability of automatic tumor segmentation by multiparametric cluster analysis that uses intrinsic MRI contrast only. MATERIALS AND METHODS Multiparametric MRI consisting of quantitative T1, T2, and apparent diffusion coefficient (ADC) mapping was performed in mice bearing subcutaneous tumors (n = 21). k-means and fuzzy c-means clustering with all possible combinations of MRI parameters, i.e. feature vectors, and 2-7 clusters were performed on the multiparametric data. Clusters associated with tumor tissue were selected on the basis of the relative signal intensity of tumor tissue in T2-weighted images. The optimum segmentation method was determined by quantitative comparison of automatic segmentation with manual segmentation performed by three observers. In addition, the automatically segmented tumor volumes from seven separate tumor data sets were quantitatively compared with histology-derived tumor volumes. RESULTS The highest similarity index between manual and automatic segmentation (SI manual,automatic = 0.82 ± 0.06) was observed for k-means clustering with feature vector {T2, ADC} and four clusters. A strong linear correlation between automatically and manually segmented tumor volumes (R (2) = 0.99) was observed for this segmentation method. Automatically segmented tumor volumes also correlated strongly with histology-derived tumor volumes (R (2) = 0.96). CONCLUSION Automatic segmentation of mouse subcutaneous tumors can be achieved on the basis of endogenous MR contrast only.
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Affiliation(s)
- Stefanie J C G Hectors
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands,
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Huang M, Yang W, Wu Y, Jiang J, Chen W, Feng Q. Brain Tumor Segmentation Based on Local Independent Projection-Based Classification. IEEE Trans Biomed Eng 2014; 61:2633-45. [DOI: 10.1109/tbme.2014.2325410] [Citation(s) in RCA: 120] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Ou Y, Akbari H, Bilello M, Da X, Davatzikos C. Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:2039-65. [PMID: 24951685 PMCID: PMC4371548 DOI: 10.1109/tmi.2014.2330355] [Citation(s) in RCA: 98] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms' similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations.
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Jones TL, Byrnes TJ, Yang G, Howe FA, Bell BA, Barrick TR. Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique. Neuro Oncol 2014; 17:466-76. [PMID: 25121771 PMCID: PMC4483092 DOI: 10.1093/neuonc/nou159] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Accepted: 07/07/2014] [Indexed: 11/29/2022] Open
Abstract
Background There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. Methods DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. Results Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. Conclusions D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning.
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Affiliation(s)
- Timothy L Jones
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
| | - Tiernan J Byrnes
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
| | - Guang Yang
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
| | - Franklyn A Howe
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
| | - B Anthony Bell
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
| | - Thomas R Barrick
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
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Ghosh N, Sun Y, Bhanu B, Ashwal S, Obenaus A. Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images. Med Image Anal 2014; 18:1059-69. [PMID: 25000294 DOI: 10.1016/j.media.2014.05.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2013] [Revised: 04/23/2014] [Accepted: 05/10/2014] [Indexed: 11/30/2022]
Abstract
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects.
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Affiliation(s)
- Nirmalya Ghosh
- Department of Pediatrics, Loma Linda University, School of Medicine, Loma Linda, CA 92354, USA
| | - Yu Sun
- Center for Research in Intelligent Systems (CRIS), University of California, Riverside, CA 92521, USA
| | - Bir Bhanu
- Center for Research in Intelligent Systems (CRIS), University of California, Riverside, CA 92521, USA
| | - Stephen Ashwal
- Department of Pediatrics, Loma Linda University, School of Medicine, Loma Linda, CA 92354, USA
| | - Andre Obenaus
- Department of Pediatrics, Loma Linda University, School of Medicine, Loma Linda, CA 92354, USA; Cell, Molecular and Developmental Biology Program and Department of Neuroscience, University of California, 1140 Bachelor Hall, Riverside, CA 92521, USA.
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26
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Weizman L, Sira LB, Joskowicz L, Rubin DL, Yeom KW, Constantini S, Shofty B, Bashat DB. Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies. Med Phys 2014; 41:052303. [PMID: 24784396 PMCID: PMC4000396 DOI: 10.1118/1.4871040] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 02/19/2014] [Accepted: 03/26/2014] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Tracking the progression of low grade tumors (LGTs) is a challenging task, due to their slow growth rate and associated complex internal tumor components, such as heterogeneous enhancement, hemorrhage, and cysts. In this paper, the authors show a semiautomatic method to reliably track the volume of LGTs and the evolution of their internal components in longitudinal MRI scans. METHODS The authors' method utilizes a spatiotemporal evolution modeling of the tumor and its internal components. Tumor components gray level parameters are estimated from the follow-up scan itself, obviating temporal normalization of gray levels. The tumor delineation procedure effectively incorporates internal classification of the baseline scan in the time-series as prior data to segment and classify a series of follow-up scans. The authors applied their method to 40 MRI scans of ten patients, acquired at two different institutions. Two types of LGTs were included: Optic pathway gliomas and thalamic astrocytomas. For each scan, a "gold standard" was obtained manually by experienced radiologists. The method is evaluated versus the gold standard with three measures: gross total volume error, total surface distance, and reliability of tracking tumor components evolution. RESULTS Compared to the gold standard the authors' method exhibits a mean Dice similarity volumetric measure of 86.58% and a mean surface distance error of 0.25 mm. In terms of its reliability in tracking the evolution of the internal components, the method exhibits strong positive correlation with the gold standard. CONCLUSIONS The authors' method provides accurate and repeatable delineation of the tumor and its internal components, which is essential for therapy assessment of LGTs. Reliable tracking of internal tumor components over time is novel and potentially will be useful to streamline and improve follow-up of brain tumors, with indolent growth and behavior.
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Affiliation(s)
- Lior Weizman
- School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Liat Ben Sira
- Department of Radiology, Tel Aviv Medical Center, Tel Aviv University, Tel Aviv 64239, Israel
| | - Leo Joskowicz
- School of Engineering and Computer Science and The Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Daniel L Rubin
- Department of Radiology, Stanford University, Stanford, California 94305
| | - Kristen W Yeom
- Department of Radiology, Stanford University, Stanford, California 94305
| | - Shlomi Constantini
- Tel Aviv Medical Center, Dana Children's Hospital, Tel Aviv University, Tel Aviv 64239, Israel
| | - Ben Shofty
- Tel Aviv Medical Center, Dana Children's Hospital, Tel Aviv University, Tel Aviv 64239, Israel
| | - Dafna Ben Bashat
- Tel Aviv Medical Center, Functional Brain Center, Tel Aviv University, Tel Aviv 64239, Israel
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Huo J, Okada K, van Rikxoort EM, Kim HJ, Alger JR, Pope WB, Goldin JG, Brown MS. Ensemble segmentation for GBM brain tumors on MR images using confidence-based averaging. Med Phys 2014; 40:093502. [PMID: 24007185 DOI: 10.1118/1.4817475] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Ensemble segmentation methods combine the segmentation results of individual methods into a final one, with the goal of achieving greater robustness and accuracy. The goal of this study was to develop an ensemble segmentation framework for glioblastoma multiforme tumors on single-channel T1w postcontrast magnetic resonance images. METHODS Three base methods were evaluated in the framework: fuzzy connectedness, GrowCut, and voxel classification using support vector machine. A confidence map averaging (CMA) method was used as the ensemble rule. RESULTS The performance is evaluated on a comprehensive dataset of 46 cases including different tumor appearances. The accuracy of the segmentation result was evaluated using the F1-measure between the semiautomated segmentation result and the ground truth. CONCLUSIONS The results showed that the CMA ensemble result statistically approximates the best segmentation result of all the base methods for each case.
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Affiliation(s)
- Jing Huo
- TeraRecon Inc., 4000 East 3rd Avenue, Suite 200, Foster City, California 94404, USA.
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28
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Thapaliya K, Pyun JY, Park CS, Kwon GR. Level set method with automatic selective local statistics for brain tumor segmentation in MR images. Comput Med Imaging Graph 2013; 37:522-37. [PMID: 24148784 DOI: 10.1016/j.compmedimag.2013.05.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2012] [Revised: 05/21/2013] [Accepted: 05/22/2013] [Indexed: 10/26/2022]
Abstract
The level set approach is a powerful tool for segmenting images. This paper proposes a method for segmenting brain tumor images from MR images. A new signed pressure function (SPF) that can efficiently stop the contours at weak or blurred edges is introduced. The local statistics of the different objects present in the MR images were calculated. Using local statistics, the tumor objects were identified among different objects. In this level set method, the calculation of the parameters is a challenging task. The calculations of different parameters for different types of images were automatic. The basic thresholding value was updated and adjusted automatically for different MR images. This thresholding value was used to calculate the different parameters in the proposed algorithm. The proposed algorithm was tested on the magnetic resonance images of the brain for tumor segmentation and its performance was evaluated visually and quantitatively. Numerical experiments on some brain tumor images highlighted the efficiency and robustness of this method.
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Affiliation(s)
- Kiran Thapaliya
- Department of Information and Communication Engineering, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 501-759, South Korea
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Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int J Comput Assist Radiol Surg 2013; 9:241-53. [DOI: 10.1007/s11548-013-0922-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Accepted: 07/03/2013] [Indexed: 01/10/2023]
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30
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Computer assisted diagnostic system in tumor radiography. J Med Syst 2013; 37:9938. [PMID: 23504472 DOI: 10.1007/s10916-013-9938-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2013] [Accepted: 03/06/2013] [Indexed: 10/27/2022]
Abstract
An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46%, which is a significant improvement than that of the existing results.
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31
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Xu T, Mandal M. Automatic brain tumor extraction from T1-weighted coronal MRI using fast bounding box and dynamic snake. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:444-7. [PMID: 23365924 DOI: 10.1109/embc.2012.6345963] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Brain tumor segmentation from MRI data is an important but challenging task. This paper presents an efficient and fully automatic brain tumor segmentation technique. The proposed technique includes a fuzzy C-means (FCM) based preprocessing to enhance the quality of T1-weighted coronal MR images, a fast bounding box (FBB) detection algorithm to locate a rectangle around tumor, and a new dynamic snake using modified Hausdorff distance (MHD) for the final tumor extraction.
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Affiliation(s)
- Tao Xu
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2V4, Canada.
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Gooya A, Pohl KM, Bilello M, Cirillo L, Biros G, Melhem ER, Davatzikos C. GLISTR: glioma image segmentation and registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:1941-54. [PMID: 22907965 PMCID: PMC4371551 DOI: 10.1109/tmi.2012.2210558] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
We present a generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumor as well as healthy tissue labels. The proposed method is based on the expectation maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the original atlas into one with tumor and edema adapted to best match a given set of patient's images. The modified atlas is registered into the patient space and utilized for estimating the posterior probabilities of various tissue labels. EM iteratively refines the estimates of the posterior probabilities of tissue labels, the deformation field and the tumor growth model parameters. Hence, in addition to segmentation, the proposed method results in atlas registration and a low-dimensional description of the patient scans through estimation of tumor model parameters. We validate the method by automatically segmenting 10 MR scans and comparing the results to those produced by clinical experts and two state-of-the-art methods. The resulting segmentations of tumor and edema outperform the results of the reference methods, and achieve a similar accuracy from a second human rater. We additionally apply the method to 122 patients scans and report the estimated tumor model parameters and their relations with segmentation and registration results. Based on the results from this patient population, we construct a statistical atlas of the glioma by inverting the estimated deformation fields to warp the tumor segmentations of patients scans into a common space.
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Affiliation(s)
- Ali Gooya
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Iran.
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Saha BN, Ray N, Greiner R, Murtha A, Zhang H. Quick detection of brain tumors and edemas: A bounding box method using symmetry. Comput Med Imaging Graph 2012; 36:95-107. [DOI: 10.1016/j.compmedimag.2011.06.001] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2010] [Revised: 03/12/2011] [Accepted: 06/01/2011] [Indexed: 10/18/2022]
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Hamamci A, Kucuk N, Karaman K, Engin K, Unal G. Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:790-804. [PMID: 22207638 DOI: 10.1109/tmi.2011.2181857] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.
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Affiliation(s)
- Andac Hamamci
- Faculty of Engineering and Natural Sciences at the Sabanci University, Istanbul, Turkey.
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35
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Hsieh TM, Liu YM, Liao CC, Xiao F, Chiang IJ, Wong JM. Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing. BMC Med Inform Decis Mak 2011; 11:54. [PMID: 21871082 PMCID: PMC3189096 DOI: 10.1186/1472-6947-11-54] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2010] [Accepted: 08/26/2011] [Indexed: 11/25/2022] Open
Abstract
Background In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images. This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain. Methods The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system. Results The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain. Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related. Conclusions Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use.
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Affiliation(s)
- Thomas M Hsieh
- Institute of Biomedical Engineering, and College of Medicine, National Taiwan University, Taipei
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36
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Weizman L, Ben Sira L, Joskowicz L, Constantini S, Precel R, Shofty B, Ben Bashat D. Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI. Med Image Anal 2011; 16:177-88. [PMID: 21852179 DOI: 10.1016/j.media.2011.07.001] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2010] [Revised: 06/29/2011] [Accepted: 07/04/2011] [Indexed: 10/17/2022]
Abstract
This paper presents an automatic method for the segmentation, internal classification and follow-up of optic pathway gliomas (OPGs) from multi-sequence MRI datasets. Our method starts with the automatic localization of the OPG and its core with an anatomical atlas followed by a binary voxel classification with a probabilistic tissue model whose parameters are estimated from the MR images. The method effectively incorporates prior location, tissue characteristics, and intensity information for the delineation of the OPG boundaries in a consistent and repeatable manner. Internal classification of the segmented OPG volume is then obtained with a robust method that overcomes grey-level differences between learning and testing datasets. Experimental results on 25 datasets yield a mean surface distance error of 0.73 mm as compared to manual segmentation by experienced radiologists. Our method exhibits reliable performance in OPG growth follow-up MR studies, which are crucial for monitoring disease progression. To the best of our knowledge, this is the first method that addresses automatic segmentation, internal classification, and follow-up of OPG.
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Affiliation(s)
- L Weizman
- School of Eng. and Computer Science, The Hebrew University of Jerusalem, Israel.
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37
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Pohl KM, Konukoglu E, Novellas S, Ayache N, Fedorov A, Talos IF, Golby A, Wells WM, Kikinis R, Black PM. A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients. Neurosurgery 2011; 68:225-33. [PMID: 21206318 DOI: 10.1227/neu.0b013e31820783d5] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. OBJECTIVE This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. METHODS We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. RESULTS Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. CONCLUSION The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.
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Affiliation(s)
- Kilian M Pohl
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
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Godil SS, Shamim MS, Enam SA, Qidwai U. Fuzzy logic: A "simple" solution for complexities in neurosciences? Surg Neurol Int 2011; 2:24. [PMID: 21541006 PMCID: PMC3050069 DOI: 10.4103/2152-7806.77177] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2010] [Accepted: 01/03/2011] [Indexed: 11/24/2022] Open
Abstract
Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum. Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology. Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures. Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.
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Affiliation(s)
- Saniya Siraj Godil
- Faculty of Health Sciences, Medical College, Aga Khan University, Karachi, Pakistan
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Automatic segmentation and components classification of optic pathway gliomas in MRI. ACTA ACUST UNITED AC 2010; 13:103-10. [PMID: 20879220 DOI: 10.1007/978-3-642-15705-9_13] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
We present a new method for the automatic segmentation and components classification of brain Optic Pathway Gliomas (OPGs) from multi-spectral MRI datasets. Our method accurately identifies the sharp OPG boundaries and consistently delineates the missing contours by effectively incorporating prior location, shape, and intensity information. It then classifies the segmented OPG volume into its three main components--solid, enhancing, and cyst--with a probabilistic tumor tissue model generated from training datasets that accounts for the datasets grey-level differences. Experimental results on 25 datasets yield a mean OPG boundary surface distance error of 0.73mm and mean volume overlap difference of 30.6% as compared to manual segmentation by an expert radiologist. A follow-up patient study shows high correlation between the clinical tumor progression evaluation and the component classification results. To the best of our knowledge, ours is the first method for automatic OPG segmentation and component classification that may support quantitative disease progression and treatment efficacy evaluation.
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Abstract
In this paper, we re-examine the cellular automata (CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type.
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Shamim MS, Enam SA, Qidwai U. Fuzzy Logic in neurosurgery: predicting poor outcomes after lumbar disk surgery in 501 consecutive patients. ACTA ACUST UNITED AC 2009; 72:565-72; discussion 572. [DOI: 10.1016/j.surneu.2009.07.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2009] [Accepted: 07/02/2009] [Indexed: 01/04/2023]
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CADrx for GBM Brain Tumors: Predicting Treatment Response from Changes in Diffusion-Weighted MRI. ALGORITHMS 2009. [DOI: 10.3390/a2041350] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Three-dimensional segmentation of tumors from CT image data using an adaptive fuzzy system. Comput Biol Med 2009; 39:869-78. [PMID: 19647818 DOI: 10.1016/j.compbiomed.2009.06.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2007] [Revised: 04/07/2009] [Accepted: 06/25/2009] [Indexed: 11/23/2022]
Abstract
A new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data was developed. To initialize the segmentation process, the user selects a region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI's spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy rules inference system. With a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected as tumor. This process is automatically repeated for every subsequent slice in the CT set without further user input, as the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image. Test cases success in segmenting the tumor from seven of the 10 CT datasets with <10% false positive errors and five test cases with <10% false negative errors. The consistency of the segmentation results statistics also showed a high repeatability factor, with low values of inter- and intra-user variability for both methods.
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Chao WH, Chen YY, Lin SH, Shih YYI, Tsang S. Automatic segmentation of magnetic resonance images using a decision tree with spatial information. Comput Med Imaging Graph 2008; 33:111-21. [PMID: 19097854 DOI: 10.1016/j.compmedimag.2008.10.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2007] [Revised: 10/21/2008] [Accepted: 10/30/2008] [Indexed: 10/21/2022]
Abstract
Here we proposed an automatic segmentation method based on a decision tree to classify the brain tissues in magnetic resonance (MR) images. Two types of data - phantom MR images obtained from IBSR (http://www.cma.mgh.harvard.edu/ibsr) and simulated brain MR images obtained from BrainWeb (http://www.bic.mni.mcgill.ca/brainweb) - were segmented using an automatic decision tree algorithm to obtain images with improved visual rendition. Spatial information on the general gray level (G), spatial gray level (S), and two-dimensional wavelet transform (W) was combined in-plane in two coordinate systems (Euclidean coordinates (x, y) or polar coordinates (r, theta)). The decision tree was constructed based on a binary tree with nodes created by splitting the distribution of input features of the tree. The spatial information obtained from MR images with different noise levels and inhomogeneities were segmented to compare whether the use of a decision tree improved the identification of human anatomical structures in a neuroimage. The average accuracy rates of segmentation for phantom images with a noise variation of 15 gray levels were 0.9999 and 0.9973 with spatial information (G, x, y, r, theta) and (S, x, y, r, theta), respectively, and 0.9999 and 0.9819 with spatial information (G, x, y, S, r, theta) and (W, x, y, G, r, theta). The average accuracy rates of segmentation for simulated MR images with a noise level of 5% were 0.9532 and 0.9439 with spatial information (G, x, y, r, theta) and (S, x, y, r, theta), respectively, and 0.9446 and 0.9287 with spatial information (G, x, y, S, r, theta) and (W, x, y, G, r, theta). The accuracy rates of segmentation were highest for both simulated phantom and brain MR images, having the lowest noise levels, from a reduction of overlapping gray levels in the images. The accuracies of segmentation were higher when the spatial information included the general gray level than when it included the spatial gray level, which in turn were higher than when it included the wavelet transform. Furthermore, the performance of segmentation was also evaluated with a boundary detection methodology that is based on the Hausdorff distance to compare with the mean computer to observer difference (COD) and mean interobserver difference (IOD) for gray matter (GM), white matter (WM), and all areas (ALL) from images segmented using the decision tree. The values of mean COD are similar and around 12mm for GM segmented using the decision tree. Our segmentation method based on a decision tree algorithm presented an easy way to perform automatic segmentation for both phantom and tissue regions in brain MR images.
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Affiliation(s)
- Wen-Hung Chao
- Department of Electrical and Control Engineering, National Chiao Tung University, No. 1001, Ta-Hsueh Rd., Hsinchu 300, Taiwan, ROC
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Chao WH, Chen YY, Cho CW, Lin SH, Shih YYI, Tsang S. Improving segmentation accuracy for magnetic resonance imaging using a boosted decision tree. J Neurosci Methods 2008; 175:206-17. [DOI: 10.1016/j.jneumeth.2008.08.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2008] [Revised: 07/27/2008] [Accepted: 08/01/2008] [Indexed: 11/25/2022]
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Konukoglu E, Wells WM, Novellas S, Ayache N, Kikinis R, Black PM, Pohl KM. MONITORING SLOWLY EVOLVING TUMORS. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2008; 2008:812-815. [PMID: 28593030 DOI: 10.1109/isbi.2008.4541120] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Change detection is a critical task in the diagnosis of many slowly evolving pathologies. This paper describes an approach that semi-automatically performs this task using longitudinal medical images. We are specifically interested in meningiomas, which experts often find difficult to monitor as the tumor evolution can be obscured by image artifacts. We test the method on synthetic data with known tumor growth as well as ten clinical data sets. We show that the results of our approach highly correlate with expert findings but seem to be less impacted by inter- and intra-rater variability.
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Affiliation(s)
- E Konukoglu
- Asclepios Research Project, INRIA, Sophia Antipolis, France
| | - W M Wells
- Brigham & Women's Hospital, Boston, MA
| | - S Novellas
- Asclepios Research Project, INRIA, Sophia Antipolis, France
| | - N Ayache
- Asclepios Research Project, INRIA, Sophia Antipolis, France
| | - R Kikinis
- Brigham & Women's Hospital, Boston, MA
| | - P M Black
- Brigham & Women's Hospital, Boston, MA
| | - K M Pohl
- Brigham & Women's Hospital, Boston, MA
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Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A. Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2008; 27:629-640. [PMID: 18450536 DOI: 10.1109/tmi.2007.912817] [Citation(s) in RCA: 143] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor.
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Affiliation(s)
- J J Corso
- Department of Radiological Sciences, University of California-Los Angeles, Los Angeles, CA 90095, USA.
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Segmenting Brain Tumors Using Pseudo–Conditional Random Fields. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2008 2008. [DOI: 10.1007/978-3-540-85988-8_43] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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McMillan KM, Rogers BP, Koay CG, Laird AR, Price RR, Meyerand ME. An objective method for combining multi-parametric MRI datasets to characterize malignant tumors. Med Phys 2007; 34:1053-61. [PMID: 17441252 DOI: 10.1118/1.2558301] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Medical imaging has made significant contributions to the characterization of malignant tumors. In many cases, however, maps from multiple modalities may be required for more complete tumor mapping. In this manuscript we propose an objective method for combining multiple imaging datasets with the goal of characterizing malignant tumors. We refer to the proposed technique as the percent overlap method (POM). To demonstrate the power and flexibility of the POM analysis, we present four patients with recurrent glioblastoma multiforme. Each patient had multiple magnetic resonance imaging procedures resulting in seven different parameter maps. Chemical shift imaging was used to provide three metabolite ratio maps (Cho:NAA, Cho:Cre, Lac:Cre). A perfusion scan provided regional cerebral blood volume and permeability maps. Diffusion and carbogen-based hypoxia mapping data were also acquired. Composite maps were formed for each patient using POM, then were compared to results from the ISODATA clustering technique. The POM maps of likely recurrent tumor regions were found to be consistent with the ISODATA clustering method. This manuscript presents an objective method for combining parameters from multiple physiologic imaging techniques into a single composite map. The accuracy of the map depends strongly on the sensitivity of the chosen imaging parameters to the disease process at the time of image acquisition. Further validation of this method may be achieved by correlation with histological data.
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Affiliation(s)
- Kathryn M McMillan
- Department of Radiology, Vanderbilt University, Nashville, Tennessee, USA.
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Vijayakumar C, Damayanti G, Pant R, Sreedhar CM. Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps. Comput Med Imaging Graph 2007; 31:473-84. [PMID: 17572068 DOI: 10.1016/j.compmedimag.2007.04.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2006] [Revised: 04/17/2007] [Accepted: 04/25/2007] [Indexed: 11/22/2022]
Abstract
An accurate computer-assisted method to perform segmentation of brain tumor on apparent diffusion coefficient (ADC) images and evaluate its grade (malignancy state) has been designed using a mixture of unsupervised artificial neural networks (ANN) and hierarchical multiresolution wavelet. Firstly, the ADC images are decomposed by multiresolution wavelets, which are subsequently selectively reconstructed to form wavelet filtered images. These wavelet filtered images along with FLAIR and T2 weighted images have been utilized as the features to unsupervised neural network - self organizing maps (SOM) - to segment the tumor, edema, necrosis, CSF and normal tissue and grade the malignant state of the tumor. A novel segmentation algorithm based on the number of hits experienced by Best Matching Units (BMU) on SOM maps is proposed. The results shows that the SOM performs well in differentiating the tumor, edema, necrosis, CSF and normal tissue pattern vectors on ADC images. Using the trained SOM and proposed segmentation algorithm, we are able to identify high or low grade tumor, edema, necrosis, CSF and normal tissue. The results are validated against manually segmented images and sensitivity and the specificity are observed to be 0.86 and 0.93, respectively.
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Affiliation(s)
- C Vijayakumar
- Department of Radiodiagnosis and Imaging, Armed Forces Medical College, Pune, India.
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