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Zhao S, Geng C, Guo C, Tian F, Tang X. SARU: A self-attention ResUNet to generate synthetic CT images for MR-only BNCT treatment planning. Med Phys 2023; 50:117-127. [PMID: 36129452 DOI: 10.1002/mp.15986] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/01/2022] [Accepted: 09/07/2022] [Indexed: 01/25/2023] Open
Abstract
PURPOSE Despite the significant physical differences between magnetic resonance imaging (MRI) and computed tomography (CT), the high entropy of MRI data indicates the existence of a surjective transformation from MRI to CT image. However, there is no specific optimization of the network itself in previous MRI/CT translation works, resulting in mistakes in details such as the skull margin and cavity edge. These errors might have moderate effect on conventional radiotherapy, but for boron neutron capture therapy (BNCT), the skin dose will be a critical part of the dose composition. Thus, the purpose of this work is to create a self-attention network that could directly transfer MRI to synthetical computerized tomography (sCT) images with lower inaccuracy at the skin edge and examine the viability of magnetic resonance (MR)-guided BNCT. METHODS A retrospective analysis was undertaken on 104 patients with brain malignancies who had both CT and MRI as part of their radiation treatment plan. The CT images were deformably registered to the MRI. In the U-shaped generation network, we introduced spatial and channel attention modules, as well as a versatile "Attentional ResBlock," which reduce the parameters while maintaining high performance. We employed five-fold cross-validation to test all patients, compared the proposed network to those used in earlier studies, and used Monte Carlo software to simulate the BNCT process for dosimetric evaluation in test set. RESULTS Compared with UNet, Pix2Pix, and ResNet, the mean absolute error (MAE) of self-attention ResUNet (SARU) is reduced by 12.91, 17.48, and 9.50 HU, respectively. The "two one-sided tests" show no significant difference in dose-volume histogram (DVH) results. And for all tested cases, the average 2%/2 mm gamma index of UNet, ResNet, Pix2Pix, and SARU were 0.96 ± 0.03, 0.96 ± 0.03, 0.95 ± 0.03, and 0.98 ± 0.01, respectively. The error of skin dose from SARU is much less than the results from other methods. CONCLUSIONS We have developed a residual U-shape network with an attention mechanism to generate sCT images from MRI for BNCT treatment planning with lower MAE in six organs. There is no significant difference between the dose distribution calculated by sCT and real CT. This solution may greatly simplify the BNCT treatment planning process, lower the BNCT treatment dose, and minimize image feature mismatch.
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Affiliation(s)
- Sheng Zhao
- Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Changran Geng
- Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China.,Key Laboratory of Nuclear Technology Application and Radiation Protection in Astronautics (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, Nanjing, People's Republic of China
| | - Chang Guo
- Department of Radiation Oncology, Jiangsu Cancer Hospital, Nanjing, People's Republic of China
| | - Feng Tian
- Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Xiaobin Tang
- Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China.,Key Laboratory of Nuclear Technology Application and Radiation Protection in Astronautics (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, Nanjing, People's Republic of China
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2
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Li N, Mo Y, Huang C, Han K, He M, Wang X, Wen J, Yang S, Wu H, Dong F, Sun F, Li Y, Yu Y, Zhang M, Guan X, Xu X. A Clinical Semantic and Radiomics Nomogram for Predicting Brain Invasion in WHO Grade II Meningioma Based on Tumor and Tumor-to-Brain Interface Features. Front Oncol 2021; 11:752158. [PMID: 34745982 PMCID: PMC8570084 DOI: 10.3389/fonc.2021.752158] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/04/2021] [Indexed: 01/06/2023] Open
Abstract
Background Brain invasion in meningioma has independent associations with increased risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study aimed to construct a model for predicting brain invasion in WHO grade II meningioma by using preoperative MRI. Methods One hundred seventy-three patients with brain invasion and 111 patients without brain invasion were included. Three mainstream features, namely, traditional semantic features and radiomics features from tumor and tumor-to-brain interface regions, were acquired. Predictive models correspondingly constructed on each feature set or joint feature set were constructed. Results Traditional semantic findings, e.g., peritumoral edema and other four features, had comparable performance in predicting brain invasion with each radiomics feature set. By taking advantage of semantic features and radiomics features from tumoral and tumor-to-brain interface regions, an integrated nomogram that quantifies the risk factor of each selected feature was constructed and had the best performance in predicting brain invasion (area under the curve values were 0.905 in the training set and 0.895 in the test set). Conclusions This study provided a clinically available and promising approach to predict brain invasion in WHO grade II meningiomas by using preoperative MRI.
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Affiliation(s)
- Ning Li
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Radiology, Fuyang District First People's Hospital, Hangzhou, China
| | - Yan Mo
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Chencui Huang
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Kai Han
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Mengna He
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaolan Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Siyu Yang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fei Dong
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fenglei Sun
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Yiming Li
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Yizhou Yu
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Reiche B, Moody A, Khademi A. Pathology-preserving intensity standardization framework for multi-institutional FLAIR MRI datasets. Magn Reson Imaging 2019; 62:59-69. [DOI: 10.1016/j.mri.2019.05.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 05/01/2019] [Accepted: 05/01/2019] [Indexed: 10/26/2022]
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4
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Khademi A, Reiche B, DiGregorio J, Arezza G, Moody AR. Whole volume brain extraction for multi-centre, multi-disease FLAIR MRI datasets. Magn Reson Imaging 2019; 66:116-130. [PMID: 31472262 DOI: 10.1016/j.mri.2019.08.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 05/01/2019] [Accepted: 08/15/2019] [Indexed: 11/19/2022]
Abstract
Automatic segmentation of the brain from magnetic resonance images (MRI) is a fundamental step in many neuroimaging processing frameworks. There are mature technologies for this task for T1- and T2-weighted MRI; however, a widely-accepted brain extraction method for Fluid-Attenuated Inversion Recovery (FLAIR) MRI has yet to be established. FLAIR MRI are becoming increasingly important for the analysis of neurodegenerative diseases and tools developed for this sequence would have clinical value. To maximize translation opportunities and for large scale research studies, algorithms for brain extraction in FLAIR MRI should generalize to multi-centre (MC) data. To this end, this work proposes a fully automated, whole volume brain extraction methodology for MC FLAIR MRI datasets. The framework is built using a novel standardization framework which reduces acquisition artifacts, standardizes the intensities of tissues and normalizes the spatial coordinates of brain tissue across MC datasets. Using the standardized datasets, an intuitive set of features based on intensity, spatial location and gradients are extracted and classified using a random forest (RF) classifier to segment the brain tissue class. A series of experiments were conducted to optimize classifier parameters, and to determine segmentation accuracy for standardized and unstandardized (original) data, as a function of scanner vendor, feature type and disease type. The models are trained, tested and validated on 156 image volumes (∼8000 image slices) from two multi-centre, multi-disease datasets, acquired with varying imaging parameters from 30 centres and three scanner vendors. The image datasets, denoted as CAIN and ADNI for vascular and dementia disease, respectively, represent a diverse collection of MC data to test the generalization capabilities of the proposed design. Results demonstrate the importance of standardization for segmentation of MC data, as models trained on standardized data yielded a drastic improvement in brain extraction accuracy compared to the original, unstandardized data (CAIN: DSC = 91% and ADNI: DSC = 86% vs. CAIN: 78% and ADNI: 65%). It was also found that models created from one scanner vendor based on unstandardized data yielded poor segmentation results in data acquired from other scanner vendors, which was improved through standardization. These results demonstrate that to create consistency in segmentations from multi-institutional datasets it is paramount that MC variability be mitigated to improve stability and to ensure generalization of machine learning algorithms for MRI.
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Affiliation(s)
- April Khademi
- Image Analysis in Medicine Lab (IAMLAB), Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada.
| | | | - Justin DiGregorio
- Image Analysis in Medicine Lab (IAMLAB), Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
| | - Giordano Arezza
- Image Analysis in Medicine Lab (IAMLAB), Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
| | - Alan R Moody
- Department of Medical Imaging, University of Toronto, Toronto M5S 1A1, Canada
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Danelakis A, Theoharis T, Verganelakis DA. Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging. Comput Med Imaging Graph 2018; 70:83-100. [DOI: 10.1016/j.compmedimag.2018.10.002] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 09/05/2018] [Accepted: 10/02/2018] [Indexed: 01/18/2023]
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6
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Jiang J, Liu T, Zhu W, Koncz R, Liu H, Lee T, Sachdev PS, Wen W. UBO Detector – A cluster-based, fully automated pipeline for extracting white matter hyperintensities. Neuroimage 2018; 174:539-549. [DOI: 10.1016/j.neuroimage.2018.03.050] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 03/16/2018] [Accepted: 03/21/2018] [Indexed: 11/27/2022] Open
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7
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Del Re EC, Gao Y, Eckbo R, Petryshen TL, Blokland GAM, Seidman LJ, Konishi J, Goldstein JM, McCarley RW, Shenton ME, Bouix S. A New MRI Masking Technique Based on Multi-Atlas Brain Segmentation in Controls and Schizophrenia: A Rapid and Viable Alternative to Manual Masking. J Neuroimaging 2015; 26:28-36. [PMID: 26585545 DOI: 10.1111/jon.12313] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Revised: 09/24/2015] [Accepted: 09/25/2015] [Indexed: 01/18/2023] Open
Abstract
UNLABELLED Brain masking of MRI images separates brain from surrounding tissue and its accuracy is important for further imaging analyses. We implemented a new brain masking technique based on multi-atlas brain segmentation (MABS) and compared MABS to masks generated using FreeSurfer (FS; version 5.3), Brain Extraction Tool (BET), and Brainwash, using manually defined masks (MM) as the gold standard. We further determined the effect of different masking techniques on cortical and subcortical volumes generated by FreeSurfer. METHODS Images were acquired on a 3-Tesla MR Echospeed system General Electric scanner on five control and five schizophrenia subjects matched on age, sex, and IQ. Automated masks were generated from MABS, FS, BET, and Brainwash, and compared to MM using these metrics: a) volume difference from MM; b) Dice coefficients; and c) intraclass correlation coefficients. RESULTS Mean volume difference between MM and MABS masks was significantly less than the difference between MM and FS or BET masks. Dice coefficient between MM and MABS was significantly higher than Dice coefficients between MM and FS, BET, or Brainwash. For subcortical and left cortical regions, MABS volumes were closer to MM volumes than were BET or FS volumes. For right cortical regions, MABS volumes were closer to MM volumes than were BET volumes. CONCLUSIONS Brain masks generated using FreeSurfer, BET, and Brainwash are rapidly obtained, but are less accurate than manually defined masks. Masks generated using MABS, in contrast, resemble more closely the gold standard of manual masking, thereby offering a rapid and viable alternative.
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Affiliation(s)
- Elisabetta C Del Re
- VA Boston Healthcare System, Brockton, MA.,Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Yi Gao
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Ryan Eckbo
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | | | | | - Larry J Seidman
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Jun Konishi
- VA Boston Healthcare System, Brockton, MA.,Department of Psychiatry, Harvard Medical School, Boston, MA
| | | | - Robert W McCarley
- VA Boston Healthcare System, Brockton, MA.,Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Martha E Shenton
- VA Boston Healthcare System, Brockton, MA.,Department of Psychiatry, Harvard Medical School, Boston, MA.,Department of Radiology, Harvard Medical School, Boston, MA
| | - Sylvain Bouix
- Department of Psychiatry, Harvard Medical School, Boston, MA
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8
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Wang R, Li C, Wang J, Wei X, Li Y, Hui C, Zhu Y, Zhang S. Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy. Magn Reson Imaging 2014; 32:1321-9. [DOI: 10.1016/j.mri.2014.08.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 06/23/2014] [Accepted: 08/08/2014] [Indexed: 11/17/2022]
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9
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Automatic segmentation of brain MRI through stationary wavelet transform and random forests. Pattern Anal Appl 2014. [DOI: 10.1007/s10044-014-0373-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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10
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Wang Y, Nie J, Yap PT, Li G, Shi F, Geng X, Guo L, Shen D. Knowledge-guided robust MRI brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates. PLoS One 2014; 9:e77810. [PMID: 24489639 PMCID: PMC3906014 DOI: 10.1371/journal.pone.0077810] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2013] [Accepted: 09/04/2013] [Indexed: 11/18/2022] Open
Abstract
Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55∼90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18∼96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5∼18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness.
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Affiliation(s)
- Yaping Wang
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi Province, China
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, United States of America
| | - Jingxin Nie
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, United States of America
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, United States of America
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, United States of America
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, United States of America
| | - Xiujuan Geng
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, Maryland, United States of America
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi Province, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, United States of America
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
- * E-mail:
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11
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Yang X, Fei B. Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET. J Am Med Inform Assoc 2013; 20:1037-45. [PMID: 23761683 PMCID: PMC3822115 DOI: 10.1136/amiajnl-2012-001544] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Revised: 05/03/2013] [Accepted: 05/23/2013] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Combined magnetic resonance/positron emission tomography (MR/PET) is a relatively new, hybrid imaging modality. MR-based attenuation correction often requires segmentation of the bone on MR images. In this study, we present an automatic segmentation method for the skull on MR images for attenuation correction in brain MR/PET applications. MATERIALS AND METHODS Our method transforms T1-weighted MR images to the Radon domain and then detects the features of the skull image. In the Radon domain we use a bilateral filter to construct a multiscale image series. For the repeated convolution we increase the spatial smoothing in each scale and make the width of the spatial and range Gaussian function doubled in each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The use of the multiscale bilateral filtering scheme is to improve the robustness of the method for noise MR images. After combining the two filtered sinograms, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image. RESULTS This method has been tested with brain phantom data, simulated brain data, and real MRI data. For real MRI data the Dice overlap ratios are 92.2%±1.9% between our segmentation and manual segmentation. CONCLUSIONS The multiscale segmentation method is robust and accurate and can be used for MRI-based attenuation correction in combined MR/PET.
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Affiliation(s)
- Xiaofeng Yang
- Department of Radiology and Imaging Sciences, Center for Systems Imaging, Emory University, Atlanta, Georgia, USA
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Center for Systems Imaging, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA
- Winship Cancer Institute of Emory University, Atlanta, Georgia, USA
- Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia, USA
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12
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Bijar A, Khayati R, Peñalver Benavent A. Increasing the contrast of the brain MR FLAIR images using fuzzy membership functions and structural similarity indices in order to segment MS lesions. PLoS One 2013; 8:e65469. [PMID: 23799015 PMCID: PMC3684600 DOI: 10.1371/journal.pone.0065469] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2012] [Accepted: 04/28/2013] [Indexed: 11/18/2022] Open
Abstract
Segmentation is an important step for the diagnosis of multiple sclerosis (MS). This paper presents a new approach to the fully automatic segmentation of MS lesions in Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance (MR) images. With the aim of increasing the contrast of the FLAIR MR images with respect to the MS lesions, the proposed method first estimates the fuzzy memberships of brain tissues (i.e., the cerebrospinal fluid (CSF), the normal-appearing brain tissue (NABT), and the lesion). The procedure for determining the fuzzy regions of their member functions is performed by maximizing fuzzy entropy through Genetic Algorithm. Research shows that the intersection points of the obtained membership functions are not accurate enough to segment brain tissues. Then, by extracting the structural similarity (SSIM) indices between the FLAIR MR image and its lesions membership image, a new contrast-enhanced image is created in which MS lesions have high contrast against other tissues. Finally, the new contrast-enhanced image is used to segment MS lesions. To evaluate the result of the proposed method, similarity criteria from all slices from 20 MS patients are calculated and compared with other methods, which include manual segmentation. The volume of segmented lesions is also computed and compared with Gold standard using the Intraclass Correlation Coefficient (ICC) and paired samples t test. Similarity index for the patients with small lesion load, moderate lesion load and large lesion load was 0.7261, 0.7745 and 0.8231, respectively. The average overall similarity index for all patients is 0.7649. The t test result indicates that there is no statistically significant difference between the automatic and manual segmentation. The validated results show that this approach is very promising.
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Affiliation(s)
- Ahmad Bijar
- Department of Biomedical Engineering, Shahed University, Tehran, Iran.
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Liu HT, Sheu TWH, Chang HH. Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification. Med Biol Eng Comput 2013; 51:1091-104. [DOI: 10.1007/s11517-013-1089-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Accepted: 05/25/2013] [Indexed: 10/26/2022]
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14
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Wang Y, Catindig JA, Hilal S, Soon HW, Ting E, Wong TY, Venketasubramanian N, Chen C, Qiu A. Multi-stage segmentation of white matter hyperintensity, cortical and lacunar infarcts. Neuroimage 2012; 60:2379-88. [DOI: 10.1016/j.neuroimage.2012.02.034] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2011] [Revised: 02/13/2012] [Accepted: 02/15/2012] [Indexed: 01/18/2023] Open
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15
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Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI. Comput Biol Med 2012; 42:509-22. [DOI: 10.1016/j.compbiomed.2012.01.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2011] [Accepted: 01/13/2012] [Indexed: 11/18/2022]
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16
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Brain MRI segmentation with multiphase minimal partitioning: a comparative study. Int J Biomed Imaging 2011; 2007:10526. [PMID: 18253474 PMCID: PMC2211521 DOI: 10.1155/2007/10526] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2006] [Revised: 11/10/2006] [Accepted: 12/19/2006] [Indexed: 11/18/2022] Open
Abstract
This paper presents the implementation and quantitative evaluation
of a multiphase three-dimensional deformable model in a level set
framework for automated segmentation of brain MRIs. The
segmentation algorithm performs an optimal partitioning of
three-dimensional data based on homogeneity measures that
naturally evolves to the extraction of different tissue types in
the brain. Random seed initialization was used to minimize the
sensitivity of the method to initial conditions while avoiding the
need for a priori information. This random initialization
ensures robustness of the method with respect to the
initialization and the minimization set up. Postprocessing
corrections with morphological operators were applied to refine
the details of the global segmentation method. A clinical study
was performed on a database of 10 adult brain MRI volumes to
compare the level set segmentation to three other methods:
“idealized” intensity thresholding, fuzzy connectedness, and an
expectation maximization classification using hidden Markov random
fields. Quantitative evaluation of segmentation accuracy was
performed with comparison to manual segmentation computing true
positive and false positive volume fractions. A statistical
comparison of the segmentation methods was performed through a
Wilcoxon analysis of these error rates and results showed very
high quality and stability of the multiphase three-dimensional
level set method.
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17
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Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images. Comput Biol Med 2011; 41:483-92. [DOI: 10.1016/j.compbiomed.2011.04.010] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2010] [Revised: 03/24/2011] [Accepted: 04/25/2011] [Indexed: 11/18/2022]
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18
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Bijar A, Khanloo MM, Benavent AP, Khayati R. Segmentation of MS lesions using entropy-based EM algorithm and Markov random fields. ACTA ACUST UNITED AC 2011. [DOI: 10.4236/jbise.2011.48071] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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19
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Yu X, Zhang Y, Lasky RE, Datta S, Parikh NA, Narayana PA. Comprehensive brain MRI segmentation in high risk preterm newborns. PLoS One 2010; 5:e13874. [PMID: 21079730 PMCID: PMC2975631 DOI: 10.1371/journal.pone.0013874] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2010] [Accepted: 10/14/2010] [Indexed: 11/28/2022] Open
Abstract
Most extremely preterm newborns exhibit cerebral atrophy/growth disturbances and white matter signal abnormalities on MRI at term-equivalent age. MRI brain volumes could serve as biomarkers for evaluating the effects of neonatal intensive care and predicting neurodevelopmental outcomes. This requires detailed, accurate, and reliable brain MRI segmentation methods. We describe our efforts to develop such methods in high risk newborns using a combination of manual and automated segmentation tools. After intensive efforts to accurately define structural boundaries, two trained raters independently performed manual segmentation of nine subcortical structures using axial T2-weighted MRI scans from 20 randomly selected extremely preterm infants. All scans were re-segmented by both raters to assess reliability. High intra-rater reliability was achieved, as assessed by repeatability and intra-class correlation coefficients (ICC range: 0.97 to 0.99) for all manually segmented regions. Inter-rater reliability was slightly lower (ICC range: 0.93 to 0.99). A semi-automated segmentation approach was developed that combined the parametric strengths of the Hidden Markov Random Field Expectation Maximization algorithm with non-parametric Parzen window classifier resulting in accurate white matter, gray matter, and CSF segmentation. Final manual correction of misclassification errors improved accuracy (similarity index range: 0.87 to 0.89) and facilitated objective quantification of white matter signal abnormalities. The semi-automated and manual methods were seamlessly integrated to generate full brain segmentation within two hours. This comprehensive approach can facilitate the evaluation of large cohorts to rigorously evaluate the utility of regional brain volumes as biomarkers of neonatal care and surrogate endpoints for neurodevelopmental outcomes.
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Affiliation(s)
- Xintian Yu
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America
| | - Yanjie Zhang
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America
| | - Robert E. Lasky
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America
- Center for Clinical Research and Evidence Based Medicine, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America
| | - Sushmita Datta
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America
| | - Nehal A. Parikh
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America
- * E-mail:
| | - Ponnada A. Narayana
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America
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20
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Automated segmentation method of white matter and gray matter regions with multiple sclerosis lesions in MR images. Radiol Phys Technol 2010; 4:61-72. [DOI: 10.1007/s12194-010-0106-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2010] [Revised: 08/31/2010] [Accepted: 09/01/2010] [Indexed: 10/19/2022]
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21
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Hayashi N, Sakuta K, Minehiro K, Takanaga M, Sanada S, Suzuki M, Miyati T, Yamamoto T, Matsui O. Development of identification of the central sulcus in brain magnetic resonance imaging. Radiol Phys Technol 2010; 4:53-60. [PMID: 20878510 DOI: 10.1007/s12194-010-0104-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2010] [Revised: 08/31/2010] [Accepted: 09/01/2010] [Indexed: 11/28/2022]
Abstract
Magnetic resonance imaging (MRI) is useful in the quantitative evaluation of brain atrophy, because the superior contrast resolution facilitates separation of the gray and white matter. Quantitative assessment of brain atrophy has mainly been performed by manual measurement, which requires considerable time and effort to determine the brain volume. Therefore, computer-aided quantitative measurement methods for the diagnosis of brain atrophy are required. We have developed a method of segmenting the cerebrum, cerebellum-brainstem, and temporal lobe simultaneously on MR images obtained in a single sequence. It is important to measure the volume of not only these regions but also the frontal lobe in clinical use. However, for segmenting the frontal lobe, it is necessary to identify the Sylvian fissure and the central sulcus, which represent boundaries. Here, we developed a method of identifying the central sulcus from MR images obtained with a 1.5 T MRI scanner. The brain and the cerebrospinal fluid (CSF) regions were segmented using semiautomated segmentation method on MR images. The central sulcus shows an oblique line from the inside to the outside on the convexity view. The almost straight appearance of the central sulcus was used for segmentation of the central sulcus from the segmented CSF images. The central sulcus was identified with this technique in 77% of the images obtained by all sequences. This technique for identifying the central sulcus is very important not only for volumetry, but also for clinical diagnosis.
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Affiliation(s)
- Norio Hayashi
- Department of Radiological Technology, Kanazawa University Hospital, 13-1 Takaramachi, Kanazawa, 920-8641, Japan.
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22
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Sadananthan SA, Zheng W, Chee MWL, Zagorodnov V. Skull stripping using graph cuts. Neuroimage 2010; 49:225-39. [PMID: 19732839 DOI: 10.1016/j.neuroimage.2009.08.050] [Citation(s) in RCA: 92] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2008] [Revised: 07/25/2009] [Accepted: 08/24/2009] [Indexed: 11/18/2022] Open
Affiliation(s)
- Suresh A Sadananthan
- School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore
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23
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Liu JX, Chen YS, Chen LF. Accurate and robust extraction of brain regions using a deformable model based on radial basis functions. J Neurosci Methods 2009; 183:255-66. [PMID: 19467263 DOI: 10.1016/j.jneumeth.2009.05.011] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2009] [Revised: 05/09/2009] [Accepted: 05/14/2009] [Indexed: 11/18/2022]
Abstract
Brain extraction from head magnetic resonance (MR) images is a classification problem of segmenting image volumes into brain and non-brain regions. It is a difficult task due to the convoluted brain surface and the inapparent brain/non-brain boundaries in images. This paper presents an automated, robust, and accurate brain extraction method which utilizes a new implicit deformable model to well represent brain contours and to segment brain regions from MR images. This model is described by a set of Wendland's radial basis functions (RBFs) and has the advantages of compact support property and low computational complexity. Driven by the internal force for imposing the smoothness constraint and the external force for considering the intensity contrast across boundaries, the deformable model of a brain contour can efficiently evolve from its initial state toward its target by iteratively updating the RBF locations. In the proposed method, brain contours are separately determined on 2D coronal and sagittal slices. The results from these two views are generally complementary and are thus integrated to obtain a complete 3D brain volume. The proposed method was compared to four existing methods, Brain Surface Extractor, Brain Extraction Tool, Hybrid Watershed Algorithm, and Model-based Level Set, by using two sets of MR images as well as manual segmentation results obtained from the Internet Brain Segmentation Repository. Our experimental results demonstrated that the proposed approach outperformed these four methods when jointly considering extraction accuracy and robustness.
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Affiliation(s)
- Jia-Xiu Liu
- Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
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24
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Stereological evaluation of the volume and volume fraction of intracranial structures in magnetic resonance images of patients with Alzheimer's disease. Ann Anat 2009; 191:186-95. [DOI: 10.1016/j.aanat.2008.12.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2008] [Revised: 12/16/2008] [Accepted: 12/16/2008] [Indexed: 11/19/2022]
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25
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Hayashi N, Sakuta K, Sanada S, Suzuki M, Matsuura Y, Yamamoto T, Matsui O. [Development of semi-automated segmentation of the brain and CSF Region on MR images]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2008; 64:1352-60. [PMID: 19060429 DOI: 10.6009/jjrt.64.1352] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
MR imaging (MRI) is an important method for the diagnosis of abnormalities of the brain. In the present report, a semi-automated method is presented to segment the brain and CSF region on brain MR images. MR images were obtained from 6 subjects by SE sequence and 8 subjects by GRE sequence. The semi-automated method consisted of the following three steps: (1) segmentation of the intracranial region using the region-growing technique, (2) segmentation of the brain region using histogram analysis and mathematical morphology, and (3) segmentation of the CSF region using the top-hat transformation technique. The average ratio of a correctly recognized region (CRR) between the semi-automated method and manual method was 87.9%, 85.1% for the intracranial region (IRR), and 94.8% and 86.8% for the brain region in the SE and GRE sequences.
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Affiliation(s)
- Norio Hayashi
- Department of Radiological Technology, Kanazawa University Hospital
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26
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Hu Q, Qian G, Teistler M, Huang S. Automatic and Adaptive Brain Morphometry on MR Images. Radiographics 2008; 28:345-56. [DOI: 10.1148/rg.282075083] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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27
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A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images. Comput Med Imaging Graph 2008; 32:124-33. [DOI: 10.1016/j.compmedimag.2007.10.003] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2007] [Revised: 10/17/2007] [Accepted: 10/18/2007] [Indexed: 11/20/2022]
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28
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Hayashi N, Sanada S, Suzuki M, Matsuura Y, Kawahara K, Tsujii H, Yamamoto T, Matsui O. Semiautomated volumetry of the cerebrum, cerebellum-brain stem, and temporal lobe on brain magnetic resonance images. ACTA ACUST UNITED AC 2008; 26:104-14. [DOI: 10.1007/s11604-007-0200-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2007] [Accepted: 10/17/2007] [Indexed: 10/22/2022]
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29
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Khayati R, Vafadust M, Towhidkhah F, Nabavi M. Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model. Comput Biol Med 2008; 38:379-90. [PMID: 18262511 DOI: 10.1016/j.compbiomed.2007.12.005] [Citation(s) in RCA: 124] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2007] [Revised: 12/18/2007] [Accepted: 12/19/2007] [Indexed: 11/28/2022]
Abstract
In this paper, an approach is proposed for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed approach, based on a Bayesian classifier, utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the a priori probability of each class. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the similarity criteria of different slices related to 20 MS patients were calculated. Also, volumetric comparison of lesions volume between the fully automated segmentation and the gold standard was performed using correlation coefficient (CC). The results showed a better performance for the proposed approach, compared to those of previous works.
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Affiliation(s)
- Rasoul Khayati
- Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
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30
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Balan AG, Traina AJ, Ribeiro MX, Marques PM, Traina-Jr. C. HEAD: The Human Encephalon Automatic Delimiter. ACTA ACUST UNITED AC 2007. [DOI: 10.1109/cbms.2007.54] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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31
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Yovel Y, Assaf Y. Virtual definition of neuronal tissue by cluster analysis of multi-parametric imaging (virtual-dot-com imaging). Neuroimage 2007; 35:58-69. [PMID: 17208461 DOI: 10.1016/j.neuroimage.2006.08.055] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2006] [Revised: 07/21/2006] [Accepted: 08/13/2006] [Indexed: 10/23/2022] Open
Abstract
Individual mapping of cerebral, morphological, functionally related structures using MRI was carried out using a new multi-contrast acquisition and analysis framework, called virtual-dot-com imaging. So far, conventional anatomical MRI has been able to provide gross segmentation of gray/white matter boundaries and a few sub-cortical structures. By combining a handful of imaging contrasts mechanisms (T1, T2, magnetization transfer, T2* and proton density), we were able to further segment sub-cortical tissue to its sub-nuclei arrangement, a segmentation that is difficult based on conventional, single-contrast MRI. Using an automatic four-step image and signal processing algorithm, we segmented the thalamus to at least 7 sub-nuclei with high similarity across subjects and high statistical significance within subjects (p<0.0001). The identified sub-nuclei resembled the known anatomical arrangement of the thalamus given in various atlases. Each cluster was characterized by a unique MRI contrast fingerprint. With this procedure, the weighted proportions of the different cellular compartments could be estimated, a property available to date only by histological analysis. Each sub-nucleus could be characterized in terms of normalized MRI contrast and compared to other sub-nuclei. The different weights of the contrasts (T1/T2/T2*/PD/MT, etc.) for each sub-nuclei cluster might indicate the intra-cluster morphological arrangement of the tissue that it represents. The implications of this methodology are far-ranging, from non-invasive, in vivo, individual mapping of histologically distinct brain areas to automatic identification of pathological processes.
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Affiliation(s)
- Yossi Yovel
- Department of Neurobiochemistry, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel
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32
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Chiverton J, Wells K, Lewis E, Chen C, Podda B, Johnson D. Statistical morphological skull stripping of adult and infant MRI data. Comput Biol Med 2006; 37:342-57. [PMID: 16796998 DOI: 10.1016/j.compbiomed.2006.04.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2005] [Revised: 04/09/2006] [Accepted: 04/11/2006] [Indexed: 11/22/2022]
Abstract
This paper describes a novel automatic statistical morphology skull stripper (SMSS) that uniquely exploits a statistical self-similarity measure and a 2-D brain mask to delineate the brain. The result of applying SMSS to 20 MRI data set volumes, including scans of both adult and infant subjects is also described. Quantitative performance assessment was undertaken with the use of brain masks provided by a brain segmentation expert. The performance is compared with an alternative technique known as brain extraction tool. The results suggest that SMSS is capable of skull-stripping neurological data with small amounts of over- and under-segmentation.
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Affiliation(s)
- John Chiverton
- Centre for Vision, Speech and Signal Processing, School of Electronics and Physical Sciences, University of Surrey, Surrey, UK.
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33
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Rehm K, Schaper K, Anderson J, Woods R, Stoltzner S, Rottenberg D. Putting our heads together: a consensus approach to brain/non-brain segmentation in T1-weighted MR volumes. Neuroimage 2004; 22:1262-70. [PMID: 15219598 DOI: 10.1016/j.neuroimage.2004.03.011] [Citation(s) in RCA: 50] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2003] [Revised: 02/27/2004] [Accepted: 03/03/2004] [Indexed: 11/15/2022] Open
Abstract
We describe an approach to brain extraction from T1-weighted MR volumes that uses a hierarchy of masks created by different models to form a consensus mask. The algorithm (McStrip) incorporates atlas-based extraction via nonlinear warping, intensity-threshold masking with connectivity constraints, and edge-based masking with morphological operations. Volume and boundary metrics were computed to evaluate the reproducibility and accuracy of McStrip against manual brain extraction on 38 scans from normal and ataxic subjects. McStrip masks were reproducible across six repeat scans of a normal subject and were significantly more accurate than the masks produced by any of the individual algorithmic components.
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Affiliation(s)
- Kelly Rehm
- Department of Radiology, University of Minnesota, Minneapolis, MN 55417-2309, USA.
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34
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Gispert JD, Reig S, Pascau J, Vaquero JJ, García‐Barreno P, Desco M. Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error. Hum Brain Mapp 2004; 22:133-44. [PMID: 15108301 PMCID: PMC6871800 DOI: 10.1002/hbm.20013] [Citation(s) in RCA: 57] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
This work presents a new algorithm (nonuniform intensity correction; NIC) for correction of intensity inhomogeneities in T1-weighted magnetic resonance (MR) images. The bias field and a bias-free image are obtained through an iterative process that uses brain tissue segmentation. The algorithm was validated by means of realistic phantom images and a set of 24 real images. The first evaluation phase was based on a public domain phantom dataset, used previously to assess bias field correction algorithms. NIC performed similar to previously described methods in removing the bias field from phantom images, without introduction of degradation in the absence of intensity inhomogeneity. The real image dataset was used to compare the performance of this new algorithm to that of other widely used methods (N3, SPM'99, and SPM2). This dataset included both low and high bias field images from two different MR scanners of low (0.5 T) and medium (1.5 T) static fields. Using standard quality criteria for determining the goodness of the different methods, NIC achieved the best results, correcting the images of the real MR dataset, enabling its systematic use in images from both low and medium static field MR scanners. A limitation of our method is that it might fail if the bias field is so high that the initial histogram does not show bimodal distribution for white and gray matter.
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Affiliation(s)
- Juan D. Gispert
- Laboratorio de Imagen Médica, Medicina y Cirugía Experimental, Hospital General Universitario “Gregorio Marañón,” Madrid, Spain
| | - Santiago Reig
- Laboratorio de Imagen Médica, Medicina y Cirugía Experimental, Hospital General Universitario “Gregorio Marañón,” Madrid, Spain
| | - Javier Pascau
- Laboratorio de Imagen Médica, Medicina y Cirugía Experimental, Hospital General Universitario “Gregorio Marañón,” Madrid, Spain
| | - Juan J. Vaquero
- Laboratorio de Imagen Médica, Medicina y Cirugía Experimental, Hospital General Universitario “Gregorio Marañón,” Madrid, Spain
| | - Pedro García‐Barreno
- Laboratorio de Imagen Médica, Medicina y Cirugía Experimental, Hospital General Universitario “Gregorio Marañón,” Madrid, Spain
| | - Manuel Desco
- Laboratorio de Imagen Médica, Medicina y Cirugía Experimental, Hospital General Universitario “Gregorio Marañón,” Madrid, Spain
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35
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Anbeek P, Vincken KL, van Osch MJP, Bisschops RHC, van der Grond J. Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 2004; 21:1037-44. [PMID: 15006671 DOI: 10.1016/j.neuroimage.2003.10.012] [Citation(s) in RCA: 257] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2003] [Revised: 10/08/2003] [Accepted: 10/08/2003] [Indexed: 12/25/2022] Open
Abstract
A new method has been developed for fully automated segmentation of white matter lesions (WMLs) in cranial MR imaging. The algorithm uses information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It is based on the K-Nearest Neighbor (KNN) classification technique that builds a feature space from voxel intensities and spatial information. The technique generates images representing the probability per voxel being part of a WML. By application of thresholds on these probability maps, binary segmentations can be obtained. ROC curves show that the segmentations achieve both high sensitivity and specificity. A similarity index (SI), overlap fraction (OF) and extra fraction (EF) are calculated for additional quantitative analysis of the result. The SI is also used for determination of the optimal probability threshold for generation of the binary segmentation. Using probabilistic equivalents of the SI, OF and EF, the probability maps can be evaluated directly, providing a powerful tool for comparison of different classification results. This method for automated WML segmentation reaches an accuracy that is comparable to methods for multiple sclerosis (MS) lesion segmentation and is suitable for detection of WMLs in large and longitudinal population studies.
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Affiliation(s)
- Petronella Anbeek
- Department of Radiology, Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
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36
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Cocosco CA, Zijdenbos AP, Evans AC. A fully automatic and robust brain MRI tissue classification method. Med Image Anal 2003; 7:513-27. [PMID: 14561555 DOI: 10.1016/s1361-8415(03)00037-9] [Citation(s) in RCA: 235] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A novel, fully automatic, adaptive, robust procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described in this paper. The procedure is adaptive in that it customizes a training set, by using a 'pruning' strategy, such that the classification is robust against anatomical variability and pathology. Starting from a set of samples generated from prior tissue probability maps (a 'model') in a standard, brain-based coordinate system ('stereotaxic space'), the method first reduces the fraction of incorrectly labeled samples in this set by using a minimum spanning tree graph-theoretic approach. Then, the corrected set of samples is used by a supervised kNN classifier for classifying the entire 3D image. The classification procedure is robust against variability in the image quality through a non-parametric implementation: no assumptions are made about the tissue intensity distributions. The performance of this brain tissue classification procedure is demonstrated through quantitative and qualitative validation experiments on both simulated MRI data (10 subjects) and real MRI data (43 subjects). A significant improvement in output quality was observed on subjects who exhibit morphological deviations from the model due to aging and pathology.
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Affiliation(s)
- Chris A Cocosco
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, Québec, H3A 2B4, Canada.
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37
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Shan ZY, Yue GH, Liu JZ. Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images. Neuroimage 2002; 17:1587-98. [PMID: 12414297 DOI: 10.1006/nimg.2002.1287] [Citation(s) in RCA: 77] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Current semiautomated magnetic resonance (MR)-based brain segmentation and volume measurement methods are complex and not sufficiently accurate for certain applications. We have developed a simpler, more accurate automated algorithm for whole-brain segmentation and volume measurement in T(1)-weighted, three-dimensional MR images. This histogram-based brain segmentation (HBRS) algorithm is based on histograms and simple morphological operations. The algorithm's three steps are foreground/background thresholding, disconnection of brain from skull, and removal of residue fragments (sinus, cerebrospinal fluid, dura, and marrow). Brain volume was measured by counting the number of brain voxels. Accuracy was determined by applying HBRS to both simulated and real MR data. Comparing the brain volume rendered by HBRS with the volume on which the simulation is based, the average error was 1.38%. By applying HBRS to 20 normal MR data sets downloaded from the Internet Brain Segmentation Repository and comparing them with expert segmented data, the average Jaccard similarity was 0.963 and the kappa index was 0.981. The reproducibility of brain volume measurements was assessed by comparing data from two sessions (four total data sets) with human volunteers. Intrasession variability of brain volumes for sessions 1 and 2 was 0.55 +/- 0.56 and 0.74 +/- 0.56%, respectively; the mean difference between the two sessions was 0.60 +/- 0.46%. These results show that the HBRS algorithm is a simple, fast, and accurate method to determine brain volume with high reproducibility. This algorithm may be applied to various research and clinical investigations in which brain segmentation and volume measurement involving MRI data are needed.
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Affiliation(s)
- Zu Y Shan
- Department of Biomedical Engineering, The Lerner Research Institute, Cleveland, Ohio 44195, USA
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38
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Miura N, Taneda A, Shida K, Kawashima R, Kawazoe Y, Fukuda H, Shimizu T. Automatic brain tissue extraction method using erosion-dilation treatment (BREED) from three-dimensional magnetic resonance imaging T1-weighted data. J Comput Assist Tomogr 2002; 26:927-32. [PMID: 12488737 DOI: 10.1097/00004728-200211000-00012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
To improve the efficiency of brain image analysis, we propose a full-automatic method for extracting brain tissue from three-dimensional magnetic resonance imaging of T1-weighted data on the human head (brain tissue extraction method using erosion-dilation treatment [BREED]). The extraction processing is realized by combining signal intensity thresholding by means of the discriminant analysis method and an erosion-dilation treatment of the image. The accuracy of BREED is evaluated using both simulated and subject data. BREED can extract brain tissues with high accuracy (approximately 97%) for either simulated or subject data.
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Affiliation(s)
- Naoki Miura
- Department of Electronic and Information System Engineering, Faculty of Science and Technology, Hirosaki University, 3 Bunkyo-cho, Hirosaki, Aomori 036-8561, Japan
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39
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40
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Zijdenbos AP, Forghani R, Evans AC. Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2002; 21:1280-1291. [PMID: 12585710 DOI: 10.1109/tmi.2002.806283] [Citation(s) in RCA: 549] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The quantitative analysis of magnetic resonance imaging (MRI) data has become increasingly important in both research and clinical studies aiming at human brain development, function, and pathology. Inevitably, the role of quantitative image analysis in the evaluation of drug therapy will increase, driven in part by requirements imposed by regulatory agencies. However, the prohibitive length of time involved and the significant intraand inter-rater variability of the measurements obtained from manual analysis of large MRI databases represent major obstacles to the wider application of quantitative MRI analysis. We have developed a fully automatic "pipeline" image analysis framework and have successfully applied it to a number of large-scale, multicenter studies (more than 1,000 MRI scans). This pipeline system is based on robust image processing algorithms, executed in a parallel, distributed fashion. This paper describes the application of this system to the automatic quantification of multiple sclerosis lesion load in MRI, in the context of a phase III clinical trial. The pipeline results were evaluated through an extensive validation study, revealing that the obtained lesion measurements are statistically indistinguishable from those obtained by trained human observers. Given that intra- and inter-rater measurement variability is eliminated by automatic analysis, this system enhances the ability to detect small treatment effects not readily detectable through conventional analysis techniques. While useful for clinical trial analysis in multiple sclerosis, this system holds widespread potential for applications in other neurological disorders, as well as for the study of neurobiology in general.
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Affiliation(s)
- Alex P Zijdenbos
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, WB-208, Montreal, QC H3A 2B4, Canada.
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41
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He R, Narayana PA. Automatic delineation of Gd enhancements on magnetic resonance images in multiple sclerosis. Med Phys 2002; 29:1536-46. [PMID: 12148736 DOI: 10.1118/1.1487422] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
A method for automatic identification and delineation of contrast-enhanced multiple sclerosis (MS) lesions on brain magnetic resonance images is described. This method relies on adaptive local segmentation derived from the morphological "open" and "reconstruction" operations on gray scale images for identification of both lesion and nonlesion enhancements. Nonlesion enhancements from vasculature and extrameningeal tissues are identified by exploiting their topologic relationship to the brain mask. Enhancing structures without a blood-brain-barrier, such as choroid plexus, are identified and eliminated by spatially mapping the locations of the MS lesions visualized on dual echo images onto the post-contrast images. Delineation of enhancements is realized using fuzzy connectivity. Both the detection and delineation results are validated using statistical methods.
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Affiliation(s)
- Renjie He
- Department of Radiology, University of Texas at Houston Medical School, 77030, USA
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42
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Stokking R, Zuiderveld KJ, Viergever MA. Integrated volume visualization of functional image data and anatomical surfaces using normal fusion. Hum Brain Mapp 2001. [DOI: 10.1002/1097-0193(200104)12:4<203::aid-hbm1016>3.0.co;2-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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