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Abbasi S, Mehdizadeh A, Boveiri HR, Mosleh Shirazi MA, Javidan R, Khayami R, Tavakoli M. Unsupervised deep learning registration model for multimodal brain images. J Appl Clin Med Phys 2023; 24:e14177. [PMID: 37823748 PMCID: PMC10647957 DOI: 10.1002/acm2.14177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/29/2023] [Accepted: 09/14/2023] [Indexed: 10/13/2023] Open
Abstract
Multimodal image registration is a key for many clinical image-guided interventions. However, it is a challenging task because of complicated and unknown relationships between different modalities. Currently, deep supervised learning is the state-of-theart method at which the registration is conducted in end-to-end manner and one-shot. Therefore, a huge ground-truth data is required to improve the results of deep neural networks for registration. Moreover, supervised methods may yield models that bias towards annotated structures. Here, to deal with above challenges, an alternative approach is using unsupervised learning models. In this study, we have designed a novel deep unsupervised Convolutional Neural Network (CNN)-based model based on computer tomography/magnetic resonance (CT/MR) co-registration of brain images in an affine manner. For this purpose, we created a dataset consisting of 1100 pairs of CT/MR slices from the brain of 110 neuropsychic patients with/without tumor. At the next step, 12 landmarks were selected by a well-experienced radiologist and annotated on each slice resulting in the computation of series of metrics evaluation, target registration error (TRE), Dice similarity, Hausdorff, and Jaccard coefficients. The proposed method could register the multimodal images with TRE 9.89, Dice similarity 0.79, Hausdorff 7.15, and Jaccard 0.75 that are appreciable for clinical applications. Moreover, the approach registered the images in an acceptable time 203 ms and can be appreciable for clinical usage due to the short registration time and high accuracy. Here, the results illustrated that our proposed method achieved competitive performance against other related approaches from both reasonable computation time and the metrics evaluation.
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Affiliation(s)
- Samaneh Abbasi
- Department of Medical Physics and EngineeringSchool of MedicineShiraz University of Medical SciencesShirazIran
| | - Alireza Mehdizadeh
- Research Center for Neuromodulation and PainShiraz University of Medical SciencesShirazIran
| | - Hamid Reza Boveiri
- Department of Computer Engineering and ITShiraz University of TechnologyShirazIran
| | - Mohammad Amin Mosleh Shirazi
- Ionizing and Non‐Ionizing Radiation Protection Research Center, School of Paramedical SciencesShiraz University of Medical SciencesShirazIran
| | - Reza Javidan
- Department of Computer Engineering and ITShiraz University of TechnologyShirazIran
| | - Raouf Khayami
- Department of Computer Engineering and ITShiraz University of TechnologyShirazIran
| | - Meysam Tavakoli
- Department of Radiation Oncologyand Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
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2
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Barzegar Z, Jamzad M. An Efficient Optimization Approach for Glioma Tumor Segmentation in Brain MRI. J Digit Imaging 2022; 35:1634-1647. [PMID: 35995900 PMCID: PMC9712883 DOI: 10.1007/s10278-022-00655-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/22/2022] [Accepted: 05/06/2022] [Indexed: 11/29/2022] Open
Abstract
Glioma is an aggressive type of cancer that develops in the brain or spinal cord. Due to many differences in its shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding cancerous tissues is a challenging task. In recent researches, the combination of multi-atlas segmentation and machine learning methods provides robust and accurate results by learning from annotated atlas datasets. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training phase of learning methods, we proposed a semi-supervised unified framework for multi-label segmentation that formulates this problem in terms of a Markov Random Field energy optimization on a parametric graph. To evaluate the proposed framework, we apply it to publicly available BRATS datasets, including low- and high-grade glioma tumors. Experimental results indicate competitive performance compared to the state-of-the-art methods. Compared with the top ranked methods, the proposed framework obtains the best dice score for segmenting of "whole tumor" (WT), "tumor core" (TC ) and "enhancing active tumor" (ET) regions. The achieved accuracy is 94[Formula: see text] characterized by the mean dice score. The motivation of using MRF graph is to map the segmentation problem to an optimization model in a graphical environment. Therefore, by defining perfect graph structure and optimum constraints and flows in the continuous max-flow model, the segmentation is performed precisely.
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Affiliation(s)
- Zeynab Barzegar
- Present Address: Sharif University of Technology, Tehran, Iran
| | - Mansour Jamzad
- Present Address: Sharif University of Technology, Tehran, Iran
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3
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Amorosino G, Peruzzo D, Redaelli D, Olivetti E, Arrigoni F, Avesani P. DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients. Neuroimage 2022; 260:119486. [PMID: 35843515 DOI: 10.1016/j.neuroimage.2022.119486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 06/30/2022] [Accepted: 07/13/2022] [Indexed: 10/17/2022] Open
Abstract
T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis.
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Affiliation(s)
- Gabriele Amorosino
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy.
| | - Denis Peruzzo
- Neuroimaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | | | - Emanuele Olivetti
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy
| | - Filippo Arrigoni
- Paediatric Radiology and Neuroradiology Department, V. Buzzi Children's Hospital, Milan, Italy
| | - Paolo Avesani
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy
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4
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GSCFN: A graph self-construction and fusion network for semi-supervised brain tissue segmentation in MRI. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.05.047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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5
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Moazami F, Lefevre-Utile A, Papaloukas C, Soumelis V. Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images. Front Immunol 2021; 12:700582. [PMID: 34456913 PMCID: PMC8385534 DOI: 10.3389/fimmu.2021.700582] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) is one of the most common autoimmune diseases which is commonly diagnosed and monitored using magnetic resonance imaging (MRI) with a combination of clinical manifestations. The purpose of this review is to highlight the main applications of Machine Learning (ML) models and their performance in the MS field using MRI. We reviewed the articles of the last decade and grouped them based on the applications of ML in MS using MRI data into four categories: 1) Automated diagnosis of MS, 2) Prediction of MS disease progression, 3) Differentiation of MS stages, 4) Differentiation of MS from similar disorders.
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Affiliation(s)
- Faezeh Moazami
- Université de Paris, Institut de Recherche Saint-Louis, Institut National de la Santé et de la Recherche Médicale (INSERM) U976, Hôpital Saint-Louis, Paris, France
| | - Alain Lefevre-Utile
- Université de Paris, Institut de Recherche Saint-Louis, Institut National de la Santé et de la Recherche Médicale (INSERM) U976, Hôpital Saint-Louis, Paris, France.,Université Paris-Saclay, Saint Aubin, France.,Assistance Publique Hopitaux de Paris (APHP), General Pediatric and Pediatric Emergency Department, Jean Verdier Hospital, Bondy, France
| | - Costas Papaloukas
- Department of Biological Applications and Technology, University of Ioannina, Ioannina, Greece
| | - Vassili Soumelis
- Université de Paris, Institut de Recherche Saint-Louis, Institut National de la Santé et de la Recherche Médicale (INSERM) U976, Hôpital Saint-Louis, Paris, France.,Assistance Publique Hopitaux de Paris (APHP), Hôpital Saint-Louis, Immunology-Histocompatibility Department, Paris, France
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6
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Dewey BE, Xu X, Knutsson L, Jog A, Prince JL, Barker PB, van Zijl PCM, Leigh R, Nyquist P. MTT and Blood-Brain Barrier Disruption within Asymptomatic Vascular WM Lesions. AJNR Am J Neuroradiol 2021; 42:1396-1402. [PMID: 34083262 PMCID: PMC8367617 DOI: 10.3174/ajnr.a7165] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/13/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND AND PURPOSE White matter lesions of presumed ischemic origin are associated with progressive cognitive impairment and impaired BBB function. Studying the longitudinal effects of white matter lesion biomarkers that measure changes in perfusion and BBB patency within white matter lesions is required for long-term studies of lesion progression. We studied perfusion and BBB disruption within white matter lesions in asymptomatic subjects. MATERIALS AND METHODS Anatomic imaging was followed by consecutive dynamic contrast-enhanced and DSC imaging. White matter lesions in 21 asymptomatic individuals were determined using a Subject-Specific Sparse Dictionary Learning algorithm with manual correction. Perfusion-related parameters including CBF, MTT, the BBB leakage parameter, and volume transfer constant were determined. RESULTS MTT was significantly prolonged (7.88 [SD, 1.03] seconds) within white matter lesions compared with normal-appearing white (7.29 [SD, 1.14] seconds) and gray matter (6.67 [SD, 1.35] seconds). The volume transfer constant, measured by dynamic contrast-enhanced imaging, was significantly elevated (0.013 [SD, 0.017] minutes-1) in white matter lesions compared with normal-appearing white matter (0.007 [SD, 0.011] minutes-1). BBB disruption within white matter lesions was detected relative to normal white and gray matter using the DSC-BBB leakage parameter method so that increasing BBB disruption correlated with increasing white matter lesion volume (Spearman correlation coefficient = 0.44; P < .046). CONCLUSIONS A dual-contrast-injection MR imaging protocol combined with a 3D automated segmentation analysis pipeline was used to assess BBB disruption in white matter lesions on the basis of quantitative perfusion measures including the volume transfer constant (dynamic contrast-enhanced imaging), the BBB leakage parameter (DSC), and MTT (DSC). This protocol was able to detect early pathologic changes in otherwise healthy individuals.
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Affiliation(s)
- B E Dewey
- From the Department of Electrical and Computer Engineering (B.E.D., J.L.P.), Johns Hopkins University, Baltimore, Maryland
- F.M. Kirby Research Center for Functional Brain Imaging (B.E.D., X.X., P.B.B., P.C.M.v.Z.), Kennedy Krieger Institute, Baltimore, Maryland
| | - X Xu
- F.M. Kirby Research Center for Functional Brain Imaging (B.E.D., X.X., P.B.B., P.C.M.v.Z.), Kennedy Krieger Institute, Baltimore, Maryland
- Department of Radiology and Radiological Science (X.X., L.K., J.L.P., P.B.B., P.C.M.v.Z.), Division of MRI Research, Johns Hopkins University, Baltimore, Maryland
| | - L Knutsson
- Department of Radiology and Radiological Science (X.X., L.K., J.L.P., P.B.B., P.C.M.v.Z.), Division of MRI Research, Johns Hopkins University, Baltimore, Maryland
- Department of Medical Radiation Physics (L.K.), Lund University, Lund, Sweden
| | - A Jog
- Athinoula A. Martinos Center for Biomedical Imaging (A.J.), Harvard University Medical School, Boston Massachusetts
| | - J L Prince
- From the Department of Electrical and Computer Engineering (B.E.D., J.L.P.), Johns Hopkins University, Baltimore, Maryland
- Department of Radiology and Radiological Science (X.X., L.K., J.L.P., P.B.B., P.C.M.v.Z.), Division of MRI Research, Johns Hopkins University, Baltimore, Maryland
| | - P B Barker
- F.M. Kirby Research Center for Functional Brain Imaging (B.E.D., X.X., P.B.B., P.C.M.v.Z.), Kennedy Krieger Institute, Baltimore, Maryland
- Department of Radiology and Radiological Science (X.X., L.K., J.L.P., P.B.B., P.C.M.v.Z.), Division of MRI Research, Johns Hopkins University, Baltimore, Maryland
| | - P C M van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging (B.E.D., X.X., P.B.B., P.C.M.v.Z.), Kennedy Krieger Institute, Baltimore, Maryland
- Department of Radiology and Radiological Science (X.X., L.K., J.L.P., P.B.B., P.C.M.v.Z.), Division of MRI Research, Johns Hopkins University, Baltimore, Maryland
| | - R Leigh
- Department of Neurology (R.L., P.N.), Electrical and Computer Engineering (B.E.D., J.L.P.), Johns Hopkins University, Baltimore, Maryland
| | - P Nyquist
- Department of Neurology (R.L., P.N.), Electrical and Computer Engineering (B.E.D., J.L.P.), Johns Hopkins University, Baltimore, Maryland
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7
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Mishro PK, Agrawal S, Panda R, Abraham A. A Novel Type-2 Fuzzy C-Means Clustering for Brain MR Image Segmentation. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:3901-3912. [PMID: 32568716 DOI: 10.1109/tcyb.2020.2994235] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The fuzzy C -means (FCM) clustering procedure is an unsupervised form of grouping the homogenous pixels of an image in the feature space into clusters. A brain magnetic resonance (MR) image is affected by noise and intensity inhomogeneity (IIH) during the acquisition process. FCM has been used in MR brain tissue segmentation. However, it does not consider the neighboring pixels for computing the membership values, thereby misclassifying the noisy pixels. The inaccurate cluster centers obtained in FCM do not address the problem of IIH. A fixed value of the fuzzifier ( m ) used in FCM brings uncertainty in controlling the fuzziness of the extracted clusters. To resolve these issues, we suggest a novel type-2 adaptive weighted spatial FCM (AWSFCM) clustering algorithm for MR brain tissue segmentation. The idea of type-2 FCM applied to the problem on hand is new and is reported in this article. The application of the proposed technique to the problem of MR brain tissue segmentation replaces the fixed fuzzifier value with a fuzzy linguistic fuzzifier value ( M ). The introduction of the spatial information in the membership function reduces the misclassification of noisy pixels. Furthermore, the incorporation of adaptive weights into the cluster center update function improves the accuracy of the final cluster centers, thereby reducing the effect of IIH. The suggested algorithm is evaluated using T1-w, T2-w, and proton density (PD) brain MR image slices. The performance is justified in terms of qualitative and quantitative measures followed by statistical analysis. The outcomes demonstrate the superiority and robustness of the algorithm in comparison to the state-of-the-art methods. This article is useful for the cybernetics application.
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8
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Gordon S, Kodner B, Goldfryd T, Sidorov M, Goldberger J, Raviv TR. An atlas of classifiers-a machine learning paradigm for brain MRI segmentation. Med Biol Eng Comput 2021; 59:1833-1849. [PMID: 34313921 DOI: 10.1007/s11517-021-02414-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 04/21/2021] [Indexed: 11/25/2022]
Abstract
We present the Atlas of Classifiers (AoC)-a conceptually novel framework for brain MRI segmentation. The AoC is a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights, a few for each voxel, represent the training dataset. It can, therefore, be considered as a light-weight learning machine, which despite its low capacity does not underfit the problem. The AoC construction is independent of the actual intensities of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities. In this sense, it does not overfit the training data, as well. The proposed method has been applied to numerous publicly available datasets for the segmentation of brain MRI tissues and is shown to be robust to noise and outreach commonly used methods. Promising results were also obtained for multi-modal, cross-modality MRI segmentation. Finally, we show how AoC trained on brain MRIs of healthy subjects can be exploited for lesion segmentation of multiple sclerosis patients.
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Affiliation(s)
- Shiri Gordon
- The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Boris Kodner
- The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Tal Goldfryd
- The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Michael Sidorov
- The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Jacob Goldberger
- The Faculty of Electrical Engineering, Ber-Ilan University, Ramat-Gan, Israel
| | - Tammy Riklin Raviv
- The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
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9
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Ma M, Mei S, Wan S, Wang Z, Ge Z, Lam V, Feng D. Keyframe Extraction From Laparoscopic Videos via Diverse and Weighted Dictionary Selection. IEEE J Biomed Health Inform 2021; 25:1686-1698. [PMID: 32841131 DOI: 10.1109/jbhi.2020.3019198] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Laparoscopic videos have been increasingly acquired for various purposes including surgical training and quality assurance, due to the wide adoption of laparoscopy in minimally invasive surgeries. However, it is very time consuming to view a large amount of laparoscopic videos, which prevents the values of laparoscopic video archives from being well exploited. In this paper, a dictionary selection based video summarization method is proposed to effectively extract keyframes for fast access of laparoscopic videos. Firstly, unlike the low-level feature used in most existing summarization methods, deep features are extracted from a convolutional neural network to effectively represent video frames. Secondly, based on such a deep representation, laparoscopic video summarization is formulated as a diverse and weighted dictionary selection model, in which image quality is taken into account to select high quality keyframes, and a diversity regularization term is added to reduce redundancy among the selected keyframes. Finally, an iterative algorithm with a rapid convergence rate is designed for model optimization, and the convergence of the proposed method is also analyzed. Experimental results on a recently released laparoscopic dataset demonstrate the clear superiority of the proposed methods. The proposed method can facilitate the access of key information in surgeries, training of junior clinicians, explanations to patients, and archive of case files.
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10
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Huang C, Xiang Z, Zhang Y, Tan DS, Yip CK, Liu Z, Li Y, Yu S, Diao L, Wong LY, Ling WL, Zeng Y, Tu W. Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients. Front Immunol 2021; 12:642167. [PMID: 33868275 PMCID: PMC8047052 DOI: 10.3389/fimmu.2021.642167] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 02/15/2021] [Indexed: 12/13/2022] Open
Abstract
Recurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely associated with the maternal environment, which is, in turn, affected by complex immune factors. Without the use of automated tools, it is often difficult to assess the interaction and synergistic effects of the various immune factors on the pregnancy outcome. As a result, the application of Artificial Intelligence (A.I.) has been explored in the field of assisted reproductive technology (ART). In this study, we reviewed studies on the use of A.I. to develop prediction models for pregnancy outcomes of patients who underwent ART treatment. A limited amount of models based on genetic markers or common indices have been established for prediction of pregnancy outcome of patients with RRF. In this study, we applied A.I. to analyze the medical information of patients with RRF, including immune indicators. The entire clinical samples set (561 samples) was divided into two sets: 90% of the set was used for training and 10% for testing. Different data panels were established to predict pregnancy outcomes at four different gestational nodes, including biochemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth, respectively. The prediction models of pregnancy outcomes were established using sparse coding, based on six data panels: basic patient characteristics, hormone levels, autoantibodies, peripheral immunology, endometrial immunology, and embryo parameters. The six data panels covered 64 variables. In terms of biochemical pregnancy prediction, the area under curve (AUC) using the endometrial immunology panel was the largest (AUC = 0.766, accuracy: 73.0%). The AUC using the autoantibodies panel was the largest in predicting clinical pregnancy (AUC = 0.688, accuracy: 78.4%), ongoing pregnancy (AUC = 0.802, accuracy: 75.0%), and live birth (AUC = 0.909, accuracy: 89.7%). Combining the data panels did not significantly enhance the effect on prediction of all the four pregnancy outcomes. These results give us a new insight on reproductive immunology and establish the basis for assisting clinicians to plan more precise and personalized diagnosis and treatment for patients with RRF.
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Affiliation(s)
- Chunyu Huang
- Department of Pediatric and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Shenzhen Key Laboratory of Reproductive Immunology for Peri-Implantation, Fertility Center, Shenzhen Zhongshan Urology Hospital, Shenzhen, China
| | - Zheng Xiang
- Department of Pediatric and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Yongnu Zhang
- Shenzhen Key Laboratory of Reproductive Immunology for Peri-Implantation, Fertility Center, Shenzhen Zhongshan Urology Hospital, Shenzhen, China
| | | | | | - Zhiqiang Liu
- Shenzhen Key Laboratory of Reproductive Immunology for Peri-Implantation, Fertility Center, Shenzhen Zhongshan Urology Hospital, Shenzhen, China
| | - Yuye Li
- Shenzhen Key Laboratory of Reproductive Immunology for Peri-Implantation, Fertility Center, Shenzhen Zhongshan Urology Hospital, Shenzhen, China
| | - Shuyi Yu
- Shenzhen Key Laboratory of Reproductive Immunology for Peri-Implantation, Fertility Center, Shenzhen Zhongshan Urology Hospital, Shenzhen, China
| | - Lianghui Diao
- Shenzhen Key Laboratory of Reproductive Immunology for Peri-Implantation, Fertility Center, Shenzhen Zhongshan Urology Hospital, Shenzhen, China
| | | | | | - Yong Zeng
- Shenzhen Key Laboratory of Reproductive Immunology for Peri-Implantation, Fertility Center, Shenzhen Zhongshan Urology Hospital, Shenzhen, China
| | - Wenwei Tu
- Department of Pediatric and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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11
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Hartmann M, Fenton N, Dobson R. Current review and next steps for artificial intelligence in multiple sclerosis risk research. Comput Biol Med 2021; 132:104337. [PMID: 33773193 DOI: 10.1016/j.compbiomed.2021.104337] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 12/30/2022]
Abstract
In the last few decades, the prevalence of multiple sclerosis (MS), a chronic inflammatory disease of the nervous system, has increased, particularly in Northern European countries, the United States, and United Kingdom. The promise of artificial intelligence (AI) and machine learning (ML) as tools to address problems in MS research has attracted increasing interest in these methods. Bayesian networks offer a clear advantage since they can integrate data and causal knowledge allowing for visualizing interactions between dependent variables and potential confounding factors. A review of AI/ML research methods applied to MS found 216 papers using terms "Multiple Sclerosis", "machine learning", "artificial intelligence", "Bayes", and "Bayesian", of which 90 were relevant and recently published. More than half of these involve the detection and segmentation of MS lesions for quantitative analysis; however clinical and lifestyle risk factor assessment and prediction have largely been ignored. Of those that address risk factors, most provide only association studies for some factors and often fail to include the potential impact of confounding factors and bias (especially where these have causal explanations) that could affect data interpretation, such as reporting quality and medical care access in various countries. To address these gaps in the literature, we propose a causal Bayesian network approach to assessing risk factors for MS, which can address deficiencies in current epidemiological methods of producing risk measurements and makes better use of observational data.
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Affiliation(s)
- Morghan Hartmann
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.
| | - Norman Fenton
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK
| | - Ruth Dobson
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, E1 4NS, UK
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12
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Gryska E, Schneiderman J, Björkman-Burtscher I, Heckemann RA. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ Open 2021; 11:e042660. [PMID: 33514580 PMCID: PMC7849889 DOI: 10.1136/bmjopen-2020-042660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 01/09/2021] [Accepted: 01/12/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field. DESIGN Scoping review. SETTING Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison. RESULTS Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity. CONCLUSIONS The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.
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Affiliation(s)
- Emilia Gryska
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
| | - Justin Schneiderman
- Sektionen för klinisk neurovetenskap, Goteborgs Universitet Institutionen for Neurovetenskap och fysiologi, Goteborg, Sweden
| | | | - Rolf A Heckemann
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
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13
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Mecheter I, Alic L, Abbod M, Amira A, Ji J. MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation. J Digit Imaging 2020; 33:1224-1241. [PMID: 32607906 PMCID: PMC7573060 DOI: 10.1007/s10278-020-00361-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.
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Affiliation(s)
- Imene Mecheter
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK.
- Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, Qatar.
| | - Lejla Alic
- Magnetic Detection and Imaging Group, Faculty of Science and Technology, University of Twente, Enschede, Netherlands
| | - Maysam Abbod
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK
| | - Abbes Amira
- Institute of Artificial Intelligence, De Montfort University, Leicester, UK
| | - Jim Ji
- Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, Qatar
- Department of Electrical and Computer Engineering, Texas A & M University, College Station, TX, USA
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14
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Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data-Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annu Rev Biomed Eng 2020; 22:127-153. [PMID: 32169002 PMCID: PMC9351438 DOI: 10.1146/annurev-bioeng-060418-052147] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.
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Affiliation(s)
- John A Onofrey
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut 06520, USA;
- Department of Urology, Yale School of Medicine, New Haven, Connecticut 06520, USA
| | - Lawrence H Staib
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut 06520, USA;
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06520, USA;
| | - Xiaojie Huang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut 06520, USA;
- Citadel Securities, Chicago, Illinois 60603, USA
| | - Fan Zhang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut 06520, USA;
| | - Xenophon Papademetris
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut 06520, USA;
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06520, USA;
| | - Dimitris Metaxas
- Department of Computer Science, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom
| | - James S Duncan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut 06520, USA;
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06520, USA;
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15
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Carass A, Roy S, Gherman A, Reinhold JC, Jesson A, Arbel T, Maier O, Handels H, Ghafoorian M, Platel B, Birenbaum A, Greenspan H, Pham DL, Crainiceanu CM, Calabresi PA, Prince JL, Roncal WRG, Shinohara RT, Oguz I. Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis. Sci Rep 2020; 10:8242. [PMID: 32427874 PMCID: PMC7237671 DOI: 10.1038/s41598-020-64803-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 04/20/2020] [Indexed: 11/09/2022] Open
Abstract
The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations.
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Affiliation(s)
- Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA.
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Snehashis Roy
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Adrian Gherman
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Jacob C Reinhold
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Andrew Jesson
- Centre For Intelligent Machines, McGill University, Montréal, QC, H3A 0E9, Canada
| | - Tal Arbel
- Centre For Intelligent Machines, McGill University, Montréal, QC, H3A 0E9, Canada
| | - Oskar Maier
- Institute of Medical Informatics, University of Lübeck, 23538, Lübeck, Germany
| | - Heinz Handels
- Institute of Medical Informatics, University of Lübeck, 23538, Lübeck, Germany
| | - Mohsen Ghafoorian
- Institute for Computing and Information Sciences, Radboud University, 6525, HP, Nijmegen, Netherlands
| | - Bram Platel
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6525, GA, Nijmegen, Netherlands
| | - Ariel Birenbaum
- Department of Electrical Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel
| | - Hayit Greenspan
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel
| | - Dzung L Pham
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Ciprian M Crainiceanu
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Peter A Calabresi
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - William R Gray Roncal
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics & Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ipek Oguz
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37203, USA
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16
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Valcarcel AM, Muschelli J, Pham DL, Martin ML, Yushkevich P, Brandstadter R, Patterson KR, Schindler MK, Calabresi PA, Bakshi R, Shinohara RT. TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis. Neuroimage Clin 2020; 27:102256. [PMID: 32428847 PMCID: PMC7236059 DOI: 10.1016/j.nicl.2020.102256] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 03/24/2020] [Accepted: 03/25/2020] [Indexed: 11/15/2022]
Abstract
Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods available, yet manual delineation remains the gold standard approach. Automatic approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes the Sørensen-Dice similarity coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a generalized additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Women's Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding was mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicated no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.
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Affiliation(s)
- Alessandra M Valcarcel
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.
| | - John Muschelli
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21287, United States
| | - Dzung L Pham
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, United States
| | - Melissa Lynne Martin
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Paul Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Rachel Brandstadter
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Kristina R Patterson
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Matthew K Schindler
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Peter A Calabresi
- Department of Neurology, School of Medicine Johns Hopkins University, Baltimore, MD 21287, United States
| | - Rohit Bakshi
- Department of Neurology, Brigham Women's Hospital, Harvard Medical School, Boston, MA 02115, United States; Department of Radiology, Brigham Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
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17
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Dewey BE, Zhao C, Reinhold JC, Carass A, Fitzgerald KC, Sotirchos ES, Saidha S, Oh J, Pham DL, Calabresi PA, van Zijl PCM, Prince JL. DeepHarmony: A deep learning approach to contrast harmonization across scanner changes. Magn Reson Imaging 2019; 64:160-170. [PMID: 31301354 PMCID: PMC6874910 DOI: 10.1016/j.mri.2019.05.041] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 05/30/2019] [Accepted: 05/30/2019] [Indexed: 11/16/2022]
Abstract
Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data.
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Affiliation(s)
- Blake E Dewey
- Department of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA; Kirby Center for Functional Brain Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Can Zhao
- Department of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA
| | - Jacob C Reinhold
- Department of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, USA
| | - Kathryn C Fitzgerald
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elias S Sotirchos
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Shiv Saidha
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jiwon Oh
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dzung L Pham
- Department of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA; Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Peter A Calabresi
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peter C M van Zijl
- Kirby Center for Functional Brain Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, USA; Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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18
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Label fusion method combining pixel greyscale probability for brain MR segmentation. Sci Rep 2019; 9:17987. [PMID: 31784630 PMCID: PMC6884484 DOI: 10.1038/s41598-019-54527-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/13/2019] [Indexed: 11/08/2022] Open
Abstract
Multi-atlas-based segmentation (MAS) methods have demonstrated superior performance in the field of automatic image segmentation, and label fusion is an important part of MAS methods. In this paper, we propose a label fusion method that incorporates pixel greyscale probability information. The proposed method combines the advantages of label fusion methods based on sparse representation (SRLF) and weighted voting methods using patch similarity weights (PSWV) and introduces pixel greyscale probability information to improve the segmentation accuracy. We apply the proposed method to the segmentation of deep brain tissues in challenging 3D brain MR images from publicly available IBSR datasets, including images of the thalamus, hippocampus, caudate, putamen, pallidum and amygdala. The experimental results show that the proposed method has higher segmentation accuracy and robustness than the related methods. Compared with the state-of-the-art methods, the proposed method obtains the best putamen, pallidum and amygdala segmentation results and hippocampus and caudate segmentation results that are similar to those of the comparison methods.
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19
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Chan DD, Knutsen AK, Lu YC, Yang SH, Magrath E, Wang WT, Bayly PV, Butman JA, Pham DL. Statistical Characterization of Human Brain Deformation During Mild Angular Acceleration Measured In Vivo by Tagged Magnetic Resonance Imaging. J Biomech Eng 2019; 140:2681445. [PMID: 30029236 DOI: 10.1115/1.4040230] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Indexed: 01/17/2023]
Abstract
Understanding of in vivo brain biomechanical behavior is critical in the study of traumatic brain injury (TBI) mechanisms and prevention. Using tagged magnetic resonance imaging, we measured spatiotemporal brain deformations in 34 healthy human volunteers under mild angular accelerations of the head. Two-dimensional (2D) Lagrangian strains were examined throughout the brain in each subject. Strain metrics peaked shortly after contact with a padded stop, corresponding to the inertial response of the brain after head deceleration. Maximum shear strain of at least 3% was experienced at peak deformation by an area fraction (median±standard error) of 23.5±1.8% of cortical gray matter, 15.9±1.4% of white matter, and 4.0±1.5% of deep gray matter. Cortical gray matter strains were greater in the temporal cortex on the side of the initial contact with the padded stop and also in the contralateral temporal, frontal, and parietal cortex. These tissue-level deformations from a population of healthy volunteers provide the first in vivo measurements of full-volume brain deformation in response to known kinematics. Although strains differed in different tissue type and cortical lobes, no significant differences between male and female head accelerations or strain metrics were found. These cumulative results highlight important kinematic features of the brain's mechanical response and can be used to facilitate the evaluation of computational simulations of TBI.
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Affiliation(s)
- Deva D Chan
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892
| | - Yuan-Chiao Lu
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892
| | - Sarah H Yang
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892
| | - Elizabeth Magrath
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892
| | - Wen-Tung Wang
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892
| | - Philip V Bayly
- Professor Department of Mechanical Engineering and Materials Science, Washington University at St. Louis, St. Louis, MO 63130
| | - John A Butman
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, , Bethesda, MD 20892-1182 e-mail:
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20
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Shao M, Han S, Carass A, Li X, Blitz AM, Shin J, Prince JL, Ellingsen LM. Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly. Neuroimage Clin 2019; 23:101871. [PMID: 31174103 PMCID: PMC6551563 DOI: 10.1016/j.nicl.2019.101871] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 04/20/2019] [Accepted: 05/20/2019] [Indexed: 02/01/2023]
Abstract
Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathogenesis of ventricular enlargement and elucidate novel patterns of ventriculomegaly that can be associated with different diseases. One such disease is normal pressure hydrocephalus (NPH), a chronic form of hydrocephalus in older adults that causes dementia. Automatic parcellation of the ventricular system into its sub-compartments in patients with ventriculomegaly is quite challenging due to the large variation of the ventricle shape and size. Conventional brain labeling methods are time-consuming and often fail to identify the boundaries of the enlarged ventricles. We propose a modified 3D U-Net method to perform accurate ventricular parcellation, even with grossly enlarged ventricles, from magnetic resonance images (MRIs). We validated our method on a data set of healthy controls as well as a cohort of 95 patients with NPH with mild to severe ventriculomegaly and compared with several state-of-the-art segmentation methods. On the healthy data set, the proposed network achieved mean Dice similarity coefficient (DSC) of 0.895 ± 0.03 for the ventricular system. On the NPH data set, we achieved mean DSC of 0.973 ± 0.02, which is significantly (p < 0.005) higher than four state-of-the-art segmentation methods we compared with. Furthermore, the typical processing time on CPU-base implementation of the proposed method is 2 min, which is much lower than the several hours required by the other methods. Results indicate that our method provides: 1) highly robust parcellation of the ventricular system that is comparable in accuracy to state-of-the-art methods on healthy controls; 2) greater robustness and significantly more accurate results on cases of ventricular enlargement; and 3) a tool that enables computation of novel imaging biomarkers for dilated ventricular spaces that characterize the ventricular system.
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Affiliation(s)
- Muhan Shao
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Shuo Han
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 20892, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Xiang Li
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ari M Blitz
- Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jaehoon Shin
- Department of Radiology, University of California San Francisco, San Francisco, CA 94117, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Lotta M Ellingsen
- Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
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21
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Fritz NE, Eloyan A, Glaister J, Dewey BE, Al-Louzi O, Costello MG, Chen M, Prince JL, Calabresi PA, Zackowski KM. Quantitative vibratory sensation measurement is related to sensory cortical thickness in MS. Ann Clin Transl Neurol 2019; 6:586-595. [PMID: 30911581 PMCID: PMC6414478 DOI: 10.1002/acn3.734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 12/20/2018] [Accepted: 01/17/2019] [Indexed: 11/16/2022] Open
Abstract
Objective Vibratory sensation is a quantifiable measure of physical dysfunction and is often related to spinal cord pathology; however, its association with relevant brain areas has not been fully explored. Our objective was to establish a cortical structural substrate for vibration sensation. Methods Eighty‐four individuals with multiple sclerosis (MS) (n = 54 relapsing, n = 30 progressive) and 28 controls participated in vibratory sensation threshold quantification at the great toe and a 3T MRI evaluating volume of the thalamus and cortical thickness primary and secondary sensory cortices. Results After controlling for age, sex, and disability level, vibratory sensation thresholds were significantly related to cortical thickness of the anterior cingulate (P = 0.041), parietal operculum (P = 0.022), and inferior frontal gyrus pars operculum (P = 0.044), pars orbitalis (P = 0.007), and pars triangularis (P = 0.029). Within the progressive disease subtype, there were significant relationships between vibratory sensation and thalamic volume (P = 0.039) as well as reduced inferior frontal gyrus pars operculum (P = 0.014) and pars orbitalis (P = 0.005) cortical thickness. Conclusions The data show significant independent relationships between quantitative vibratory sensation and measures of primary and secondary sensory cortices. Quantitative clinical measurement of vibratory sensation reflects pathological changes in spatially distinct brain areas and may supplement information captured by brain atrophy measures. Without overt relapses, monitoring decline in progressive forms of MS has proved challenging; quantitative clinical assessment may provide a tool to examine pathological decline in this cohort. These data suggest that quantitative clinical assessment may be a reliable way to examine pathological decline and have broader relevance to progressive forms of MS.
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Affiliation(s)
- Nora E Fritz
- Center for Movement Studies Kennedy Krieger Institute Baltimore Maryland.,Department of Physical Medicine and Rehabilitation Johns Hopkins School of Medicine Baltimore Maryland.,Program in Physical Therapy and Department of Neurology Wayne State University Detroit Michigan
| | - Ani Eloyan
- Department of Biostatistics Brown University Providence Rhode Island
| | - Jeffrey Glaister
- Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland
| | - Blake E Dewey
- Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland.,F.M. Kirby Center for Functional Brain Imaging Kennedy Krieger Institute Baltimore Maryland
| | - Omar Al-Louzi
- Department of Neurology Massachusetts General Hospital Brigham and Women's Hospital Harvard Medical School Boston Massachusetts.,Department of Neurology Johns Hopkins School of Medicine Baltimore Maryland
| | - M Gabriela Costello
- Center for Movement Studies Kennedy Krieger Institute Baltimore Maryland.,Department of Physical Medicine and Rehabilitation Johns Hopkins School of Medicine Baltimore Maryland
| | - Min Chen
- Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland.,Department of Radiology University of Pennsylvania Philadelphia Pennsylvania
| | - Jerry L Prince
- Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland
| | - Peter A Calabresi
- Department of Neurology Johns Hopkins School of Medicine Baltimore Maryland
| | - Kathleen M Zackowski
- Center for Movement Studies Kennedy Krieger Institute Baltimore Maryland.,Department of Physical Medicine and Rehabilitation Johns Hopkins School of Medicine Baltimore Maryland.,Department of Neurology Johns Hopkins School of Medicine Baltimore Maryland
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22
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Selvaganesan K, Whitehead E, DeAlwis PM, Schindler MK, Inati S, Saad ZS, Ohayon JE, Cortese ICM, Smith B, Steven Jacobson, Nath A, Reich DS, Inati S, Nair G. Robust, atlas-free, automatic segmentation of brain MRI in health and disease. Heliyon 2019; 5:e01226. [PMID: 30828660 PMCID: PMC6383003 DOI: 10.1016/j.heliyon.2019.e01226] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 01/11/2019] [Accepted: 02/07/2019] [Indexed: 12/20/2022] Open
Abstract
Background Brain- and lesion-volumes derived from magnetic resonance images (MRI) serve as important imaging markers of disease progression in neurodegenerative diseases and aging. While manual segmentation of these volumes is both tedious and impractical in large cohorts of subjects, automated segmentation methods often fail in accurate segmentation of brains with severe atrophy or high lesion loads. The purpose of this study was to develop an atlas-free brain Classification using DErivative-based Features (C-DEF), which utilizes all scans that may be acquired during the course of a routine MRI study at any center. Methods Proton-density, T2-weighted, T1-weighted, brain-free water, 3D FLAIR, 3D T2-weighted, and 3D T2*-weighted images, collected routinely on patients with neuroinflammatory diseases at the NIH, were used to optimize the C-DEF algorithm on healthy volunteers and HIV + subjects (cohort 1). First, manually marked lesions and eroded FreeSurfer brain segmentation masks (compiled into gray and white matter, globus pallidus, CSF labels) were used in training. Next, the optimized C-DEF was applied on a separate cohort of HIV + subjects (cohort two), and the results were compared with that of FreeSurfer and Lesion-TOADS. Finally, C-DEF segmentation was evaluated on subjects clinically diagnosed with various other neurological diseases (cohort three). Results C-DEF algorithm was optimized using leave-one-out cross validation on five healthy subjects (age 36 ± 11 years), and five subjects infected with HIV (age 57 ± 2.6 years) in cohort one. The optimized C-DEF algorithm outperformed FreeSurfer and Lesion-TOADS segmentation in 49 other subjects infected with HIV (cohort two, age 54 ± 6 years) in qualitative and quantitative comparisons. Although trained only on HIV brains, sensitivity to detect lesions using C-DEF increased by 45% in HTLV-I-associated myelopathy/tropical spastic paraparesis (n = 5; age 58 ± 7 years), 33% in multiple sclerosis (n = 5; 42 ± 9 years old), and 4% in subjects with polymorphism of the cytotoxic T-lymphocyte-associated protein 4 gene (n = 5; age 24 ± 12 years) compared to Lesion-TOADS. Conclusion C-DEF outperformed other segmentation algorithms in the various neurological diseases explored herein, especially in lesion segmentation. While the results reported are from routine images acquired at the NIH, the algorithm can be easily trained and optimized for any set of contrasts and protocols for wider application. We are currently exploring various technical aspects of optimal implementation of CDEF in a clinical setting and evaluating a larger cohort of patients with other neurological diseases. Improving the accuracy of brain segmentation methodology will help better understand the relationship of imaging abnormalities to clinical and neuropsychological markers in disease.
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Affiliation(s)
- Kartiga Selvaganesan
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Emily Whitehead
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Paba M DeAlwis
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Matthew K Schindler
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | | | - Ziad S Saad
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20893, USA
| | - Joan E Ohayon
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Irene C M Cortese
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Bryan Smith
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Steven Jacobson
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Avindra Nath
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Daniel S Reich
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Sara Inati
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Govind Nair
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
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23
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Carass A, Cuzzocreo JL, Han S, Hernandez-Castillo CR, Rasser PE, Ganz M, Beliveau V, Dolz J, Ben Ayed I, Desrosiers C, Thyreau B, Romero JE, Coupé P, Manjón JV, Fonov VS, Collins DL, Ying SH, Onyike CU, Crocetti D, Landman BA, Mostofsky SH, Thompson PM, Prince JL. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. Neuroimage 2018; 183:150-172. [PMID: 30099076 PMCID: PMC6271471 DOI: 10.1016/j.neuroimage.2018.08.003] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 08/03/2018] [Accepted: 08/03/2018] [Indexed: 01/26/2023] Open
Abstract
The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.
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Affiliation(s)
- Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Jennifer L Cuzzocreo
- Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Shuo Han
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 20892, USA
| | - Carlos R Hernandez-Castillo
- Consejo Nacional de Ciencia y Tecnología, Instituto de Neuroetología, Universidad Veracruzana, Xalapa, Mexico
| | - Paul E Rasser
- Priority Research Centre for Brain & Mental Health and Stroke & Brain Injury, University of Newcastle, Callaghan, NSW, Australia
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Vincent Beliveau
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jose Dolz
- Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada
| | - Ismail Ben Ayed
- Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada
| | - Christian Desrosiers
- Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada
| | - Benjamin Thyreau
- Institute of Development, Aging and Cancer, Tohoku University, Japan
| | - José E Romero
- Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain
| | - Pierrick Coupé
- University of Bordeaux, LaBRI, UMR 5800, PICTURA, Talence, F-33400, France; CNRS, LaBRI, UMR 5800, PICTURA, Talence, F-33400, France
| | - José V Manjón
- Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain
| | - Vladimir S Fonov
- Image Processing Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- Image Processing Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sarah H Ying
- Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Deana Crocetti
- Center for Neurodevelopmental Medicine and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205, USA
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37235, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental Medicine and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205, USA; Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA; Department of Psychiatry and Behavioral Sciences, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, 90292, USA; Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, University of Southern California, Los Angeles, CA, 90033, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA
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24
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Cheng J. Sparse Range-Constrained Learning and Its Application for Medical Image Grading. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2729-2738. [PMID: 29994702 DOI: 10.1109/tmi.2018.2851607] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Sparse learning has been shown to be effective in solving many real-world problems. Finding sparse representations is a fundamentally important topic in many fields of science including signal processing, computer vision, genome study, and medical imaging. One important issue in applying sparse representation is to find the basis to represent the data, especially in computer vision and medical imaging where the data are not necessary incoherent. In medical imaging, clinicians often grade the severity or measure the risk score of a disease based on images. This process is referred to as medical image grading. Manual grading of the disease severity or risk score is often used. However, it is tedious, subjective, and expensive. Sparse learning has been used for automatic grading of medical images for different diseases. In the grading, we usually begin with one step to find a sparse representation of the testing image using a set of reference images or atoms from the dictionary. Then in the second step, the selected atoms are used as references to compute the grades of the testing images. Since the two steps are conducted sequentially, the objective function in the first step is not necessarily optimized for the second step. In this paper, we propose a novel sparse range-constrained learning (SRCL) algorithm for medical image grading. Different from most of existing sparse learning algorithms, SRCL integrates the objective of finding a sparse representation and that of grading the image into one function. It aims to find a sparse representation of the testing image based on atoms that are most similar in both the data or feature representation and the medical grading scores. We apply the new proposed SRCL to two different applications, namely, cup-to-disc ratio (CDR) computation from retinal fundus images and cataract grading from slit-lamp lens images. Experimental results show that the proposed method is able to improve the accuracy in CDR computation and cataract grading.
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25
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Iqbal A, Seghouane AK. A dictionary learning algorithm for multi-subject fMRI analysis based on a hybrid concatenation scheme. DIGITAL SIGNAL PROCESSING 2018; 83:249-260. [DOI: 10.1016/j.dsp.2018.09.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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26
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Caldito NG, Saidha S, Sotirchos ES, Dewey BE, Cowley NJ, Glaister J, Fitzgerald KC, Al-Louzi O, Nguyen J, Rothman A, Ogbuokiri E, Fioravante N, Feldman S, Kwakyi O, Risher H, Kimbrough D, Frohman TC, Frohman E, Balcer L, Crainiceanu C, Van Zijl PCM, Mowry EM, Reich DS, Oh J, Pham DL, Prince J, Calabresi PA. Brain and retinal atrophy in African-Americans versus Caucasian-Americans with multiple sclerosis: a longitudinal study. Brain 2018; 141:3115-3129. [PMID: 30312381 PMCID: PMC6202573 DOI: 10.1093/brain/awy245] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 06/03/2018] [Accepted: 08/09/2018] [Indexed: 01/09/2023] Open
Abstract
On average, African Americans with multiple sclerosis demonstrate higher inflammatory disease activity, faster disability accumulation, greater visual dysfunction, more pronounced brain tissue damage and higher lesion volume loads compared to Caucasian Americans with multiple sclerosis. Neurodegeneration is an important component of multiple sclerosis, which in part accounts for the clinical heterogeneity of the disease. Brain atrophy appears to be widespread, although it is becoming increasingly recognized that regional substructure atrophy may be of greater clinical relevance. Patient race (within the limitations of self-identified ancestry) is regarded as an important contributing factor. However, there is a paucity of studies examining differences in neurodegeneration and brain substructure volumes over time in African Americans relative to Caucasian American patients. Optical coherence tomography is a non-invasive and reliable tool for measuring structural retinal changes. Recent studies support its utility for tracking neurodegeneration and disease progression in vivo in multiple sclerosis. Relative to Caucasian Americans, African American patients have been found to have greater retinal structural injury in the inner retinal layers. Increased thickness of the inner nuclear layer and the presence of microcystoid macular pathology at baseline predict clinical and radiological inflammatory activity, although whether race plays a role in these changes has not been investigated. Similarly, assessment of outer retinal changes according to race in multiple sclerosis remains incompletely characterized. Twenty-two African Americans and 60 matched Caucasian Americans with multiple sclerosis were evaluated with brain MRI, and 116 African Americans and 116 matched Caucasian Americans with multiple sclerosis were monitored with optical coherence tomography over a mean duration of 4.5 years. Mixed-effects linear regression models were used in statistical analyses. Grey matter (-0.9%/year versus -0.5%: P =0.02), white matter (-0.7%/year versus -0.3%: P =0.04) and nuclear thalamic (-1.5%/year versus -0.7%/year: P =0.02) atrophy rates were approximately twice as fast in African Americans. African Americans also exhibited higher proportions of microcystoid macular pathology (12.1% versus 0.9%, P =0.001). Retinal nerve fibre layer (-1.1% versus -0.8%: P =0.02) and ganglion cell+ inner plexiform layer (-0.7%/year versus -0.4%/year: P =0.01) atrophy rates were faster in African versus Caucasian Americans. African Americans on average exhibited more rapid neurodegeneration than Caucasian Americans and had significantly faster brain and retinal tissue loss. These results corroborate the more rapid clinical progression reported to occur, in general, in African Americans with multiple sclerosis and support the need for future studies involving African Americans in order to identify individual differences in treatment responses in multiple sclerosis.
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Affiliation(s)
| | - Shiv Saidha
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elias S Sotirchos
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Blake E Dewey
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Norah J Cowley
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jeffrey Glaister
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Kathryn C Fitzgerald
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Omar Al-Louzi
- Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - James Nguyen
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alissa Rothman
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Esther Ogbuokiri
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nicholas Fioravante
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sydney Feldman
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ohemaa Kwakyi
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hunter Risher
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dorlan Kimbrough
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Teresa C Frohman
- Department of Neurology, University of Texas Austin Dell Medical School, Austin TX, USA
| | - Elliot Frohman
- Department of Neurology, University of Texas Austin Dell Medical School, Austin TX, USA
| | - Laura Balcer
- Department of Neurology, New York University Langone Medical Center, New York, NY, USA
| | | | - Peter C M Van Zijl
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA
| | - Ellen M Mowry
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel S Reich
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins University, Baltimore MD, USA
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, USA
| | - Jiwon Oh
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division of Neurology, St. Michael’s Hospital, University of Toronto, 30 Bond Street, Toronto, Ontario, Canada
| | - Dzung L Pham
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Jerry Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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27
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Zheng Q, Wu Y, Fan Y. Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation. Front Neuroinform 2018; 12:69. [PMID: 30364123 PMCID: PMC6191508 DOI: 10.3389/fninf.2018.00069] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 09/18/2018] [Indexed: 11/26/2022] Open
Abstract
A novel label fusion method for multi-atlas based image segmentation method is developed by integrating semi-supervised and supervised machine learning techniques. Particularly, our method is developed in a pattern recognition based multi-atlas label fusion framework. We build random forests classification models for each image voxel to be segmented based on its corresponding image patches of atlas images that have been registered to the image to be segmented. The voxelwise random forests classification models are then applied to the image to be segmented to obtain a probabilistic segmentation map. Finally, a semi-supervised label propagation method is adapted to refine the probabilistic segmentation map by propagating its reliable voxelwise segmentation labels, taking into consideration consistency of local and global image appearance of the image to be segmented. The proposed method has been evaluated for segmenting the hippocampus in MR images and compared with alternative machine learning based multi-atlas based image segmentation methods. The experiment results have demonstrated that our method could obtain competitive segmentation performance (average Dice index > 0.88), compared with alternative multi-atlas based image segmentation methods under comparison. Source codes of the methods under comparison are publicly available at www.nitrc.org/frs/?group_id=1242.
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Affiliation(s)
- Qiang Zheng
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,School of Computer and Control Engineering Yantai University, Yantai, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yihong Wu
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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28
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Andersson T, Borga M, Dahlqvist Leinhard O. Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds. Pattern Recognit Lett 2018. [DOI: 10.1016/j.patrec.2018.04.037] [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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29
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Zheng Q, Fan Y. INTEGRATING SEMI-SUPERVISED LABEL PROPAGATION AND RANDOM FORESTS FOR MULTI-ATLAS BASED HIPPOCAMPUS SEGMENTATION. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:154-157. [PMID: 30079126 DOI: 10.1109/isbi.2018.8363544] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of the images to be segmented. The regression model is used to obtain reliable segmentation results to guide the label propagation for the segmentation. The proposed method has been compared with state-of-the-art multi-atlas based image segmentation methods for segmenting the hippocampus in MR images. The experiment results have demonstrated that our method obtained superior segmentation performance.
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Affiliation(s)
- Qiang Zheng
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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30
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Glaister J, Shao M, Li X, Carass A, Roy S, Blitz AM, Prince JL, Ellingsen LM. Deformable model reconstruction of the subarachnoid space. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2018; 10574. [PMID: 31043764 DOI: 10.1117/12.2293633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The subarachnoid space is a layer in the meninges that surrounds the brain and is filled with trabeculae and cerebrospinal fluid. Quantifying the volume and thickness of the subarachnoid space is of interest in order to study the pathogenesis of neurodegenerative diseases and compare with healthy subjects. We present an automatic method to reconstruct the subarachnoid space with subvoxel accuracy using a nested deformable model. The method initializes the deformable model using the convex hull of the union of the outer surfaces of the cerebrum, cerebellum and brainstem. A region force is derived from the subject's Tl-weighted and T2-weighted MRI to drive the deformable model to the outer surface of the subarachnoid space. The proposed method is compared to a semi-automatic delineation from the subject's T2-weighted MRI and an existing multi-atlas-based method. A small pilot study comparing the volume and thickness measurements in a set of age-matched subjects with normal pressure hydrocephalus and healthy controls is presented to show the efficacy of the proposed method.
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Affiliation(s)
- Jeffrey Glaister
- Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD
| | - Muhan Shao
- Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD
| | - Xiang Li
- Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD
| | - Aaron Carass
- Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD
| | | | - Ari M Blitz
- Dept. of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD
| | - Jerry L Prince
- Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD
| | - Lotta M Ellingsen
- Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD.,Dept. of Electrical and Computer Engineering, Univ. of Iceland, Reykjavik, Iceland
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31
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Shao M, Carass A, Li X, Dewey BE, Blitz AM, Prince JL, Ellingsen LM. Multi-atlas segmentation of the hydrocephalus brain using an adaptive ventricle atlas. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2018; 10578:105780F. [PMID: 34376903 PMCID: PMC8351536 DOI: 10.1117/12.2295613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Normal pressure hydrocephalus (NPH) is a brain disorder caused by disruption of the flow of cerebrospinal fluid (CSF). The dementia-like symptoms of NPH are often mistakenly attributed to Alzheimer's disease. However, if correctly diagnosed, NPH patients can potentially be treated and their symptoms reversed through surgery. Observing the dilated ventricles through magnetic resonance imaging (MRI) is one element in diagnosing NPH. Diagnostic accuracy therefore benefits from accurate, automatic parcellation of the ventricular system into its sub-compartments. We present an improvement to a whole brain segmentation approach designed for subjects with enlarged and deformed ventricles. Our method incorporates an adaptive ventricle atlas from an NPH-atlas-based segmentation as a prior and uses a more robust relaxation scheme for the multi-atlas label fusion approach that accurately labels the four sub-compartments of the ventricular system. We validated our method on NPH patients, demonstrating improvement over state-of-the-art segmentation techniques.
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Affiliation(s)
- Muhan Shao
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218
| | - Xiang Li
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Blake E Dewey
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Ari M Blitz
- Department of Radiology and Radiological Science, The Johns Hopkins University, Baltimore, MD 21287
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218
| | - Lotta M Ellingsen
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
- Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
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32
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Cherukuri V, Ssenyonga P, Warf BC, Kulkarni AV, Monga V, Schiff SJ. Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans. IEEE Trans Biomed Eng 2017; 65:1871-1884. [PMID: 29989926 DOI: 10.1109/tbme.2017.2783305] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF [before and after surgery, i.e., preoperative (pre-op) versus postoperative (post-op)] plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-op computational tomographic (CT) scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity- and feature-based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e., a training set of CT scans with labeled pixel identities is employed. METHODS Our contributions include: 1) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes; 2) quantification of associated computation and memory footprint; and 3) a customized training and test procedure for segmenting post-op hydrocephalic CT images. RESULTS Experiments performed on infant CT brain images acquired from the CURE Children's Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against a number of training samples, enhancing its deployment potential.
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Shaker F, Monadjemi SA, Alirezaie J, Naghsh-Nilchi AR. A dictionary learning approach for human sperm heads classification. Comput Biol Med 2017; 91:181-190. [DOI: 10.1016/j.compbiomed.2017.10.009] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 10/08/2017] [Accepted: 10/09/2017] [Indexed: 10/18/2022]
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Carass A, Shao M, Li X, Dewey BE, Blitz AM, Roy S, Pham DL, Prince JL, Ellingsen LM. Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients. PATCH-BASED TECHNIQUES IN MEDICAL IMAGING : THIRD INTERNATIONAL WORKSHOP, PATCH-MI 2017, HELD IN CONJUNCTION WITH MICCAI 2017, QUEBEC CITY, QC, CANADA, SEPTEMBER 14, 2017, PROCEEDINGS. PATCH-MI (WORKSHOP) (3RD : 2017 : QUEBEC, QUEBEC) 2017; 10530:20-28. [PMID: 29459902 DOI: 10.1007/978-3-319-67434-6_3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Numerous brain disorders are associated with ventriculomegaly; normal pressure hydrocephalus (NPH) is one example. NPH presents with dementia-like symptoms and is often misdiagnosed as Alzheimer's due to its chronic nature and nonspecific presenting symptoms. However, unlike other forms of dementia NPH can be treated surgically with an over 80% success rate on appropriately selected patients. Accurate assessment of the ventricles, in particular its sub-compartments, is required to diagnose the condition. Existing segmentation algorithms fail to accurately identify the ventricles in patients with such extreme pathology. We present an improvement to a whole brain segmentation approach that accurately identifies the ventricles and parcellates them into four sub-compartments. Our work is a combination of patch-based tissue segmentation and multi-atlas registration-based labeling. We include a validation on NPH patients, demonstrating superior performance against state-of-the-art methods.
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Affiliation(s)
- Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Muhan Shao
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Xiang Li
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Blake E Dewey
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ari M Blitz
- Department of Radiology and Radiological Science, The Johns Hopkins University, Baltimore, MD 21287, USA
| | - Snehashis Roy
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA
| | - Dzung L Pham
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Lotta M Ellingsen
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
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Shinohara RT, Oh J, Nair G, Calabresi PA, Davatzikos C, Doshi J, Henry RG, Kim G, Linn KA, Papinutto N, Pelletier D, Pham DL, Reich DS, Rooney W, Roy S, Stern W, Tummala S, Yousuf F, Zhu A, Sicotte NL, Bakshi R. Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis. AJNR Am J Neuroradiol 2017; 38:1501-1509. [PMID: 28642263 PMCID: PMC5557658 DOI: 10.3174/ajnr.a5254] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 04/06/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. To assess intersite variability in scan data, we imaged a volunteer with relapsing-remitting MS with a scan-rescan at each site. MATERIALS AND METHODS All imaging was acquired on Siemens scanners (4 Skyra, 2 Tim Trio, and 1 Verio). Expert segmentations were manually obtained for T1-hypointense and T2 (FLAIR) hyperintense lesions. Several automated lesion-detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied. Statistical analyses were conducted to assess variability across sites, as well as systematic biases in the volumetric measurements that were site-related. RESULTS Systematic biases due to site differences in expert-traced lesion measurements were significant (P < .01 for both T1 and T2 lesion volumes), with site explaining >90% of the variation (range, 13.0-16.4 mL in T1 and 15.9-20.1 mL in T2) in lesion volumes. Site also explained >80% of the variation in most automated volumetric measurements. Output measures clustered according to scanner models, with similar results from the Skyra versus the other 2 units. CONCLUSIONS Even in multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses.
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Affiliation(s)
- R T Shinohara
- From the Departments of Biostatistics and Epidemiology (R.T.S., K.A.L.)
| | - J Oh
- Department of Neurology (J.O., P.A.C., D.S.R.), Johns Hopkins University School of Medicine, Baltimore, Maryland.,St. Michael's Hospital (J.O.), University of Toronto, Toronto, Ontario, Canada
| | - G Nair
- Translational Neuroradiology Section (G.N., D.S.R.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
| | - P A Calabresi
- Department of Neurology (J.O., P.A.C., D.S.R.), Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - C Davatzikos
- Radiology (C.D., J.D.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - J Doshi
- Radiology (C.D., J.D.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - R G Henry
- Department of Neurology (R.G.H., N.P., W.S., A.Z.), University of California, San Francisco, San Francisco, California
| | - G Kim
- Laboratory for Neuroimaging Research (G.K., S.T., F.Y., R.B.), Partners Multiple Sclerosis Center
| | - K A Linn
- From the Departments of Biostatistics and Epidemiology (R.T.S., K.A.L.)
| | - N Papinutto
- Department of Neurology (R.G.H., N.P., W.S., A.Z.), University of California, San Francisco, San Francisco, California
| | - D Pelletier
- Department of Neurology (D.P.), Yale Medical School, New Haven, Connecticut
| | - D L Pham
- Henry M. Jackson Foundation for the Advancement of Military Medicine (D.L.P., S.R.), Bethesda, Maryland
| | - D S Reich
- Department of Neurology (J.O., P.A.C., D.S.R.), Johns Hopkins University School of Medicine, Baltimore, Maryland.,Translational Neuroradiology Section (G.N., D.S.R.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
| | - W Rooney
- Advanced Imaging Research Center, Oregon Health & Science University (W.R.), Portland, Oregon
| | - S Roy
- Henry M. Jackson Foundation for the Advancement of Military Medicine (D.L.P., S.R.), Bethesda, Maryland
| | - W Stern
- Department of Neurology (R.G.H., N.P., W.S., A.Z.), University of California, San Francisco, San Francisco, California
| | - S Tummala
- Laboratory for Neuroimaging Research (G.K., S.T., F.Y., R.B.), Partners Multiple Sclerosis Center
| | - F Yousuf
- Laboratory for Neuroimaging Research (G.K., S.T., F.Y., R.B.), Partners Multiple Sclerosis Center
| | - A Zhu
- Department of Neurology (R.G.H., N.P., W.S., A.Z.), University of California, San Francisco, San Francisco, California
| | - N L Sicotte
- Department of Neurology (N.L.S.), Cedars-Sinai Medical Center, Los Angeles, California
| | - R Bakshi
- Laboratory for Neuroimaging Research (G.K., S.T., F.Y., R.B.), Partners Multiple Sclerosis Center.,Departments of Neurology and Radiology (R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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Fritz NE, Roy S, Keller J, Prince J, Calabresi PA, Zackowski KM. Pain, cognition and quality of life associate with structural measures of brain volume loss in multiple sclerosis. NeuroRehabilitation 2017; 39:535-544. [PMID: 27689612 DOI: 10.3233/nre-161384] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Multiple sclerosis (MS) is characterized by physical and mental impairments that often result in pain and reduced quality of life. OBJECTIVE To understand the relationship of pain, quality of life, and cognition to structural measures of brain volume. METHODS Behavioral measures were assessed in a single session using standardized questionnaires and rating scales. Brain volume measures were assessed with structural magnetic resonance imaging (MRI). RESULTS Twenty-nine individuals with relapsing-remitting MS and 29 age-matched controls participated in this study. Pain, quality of life, and cognition were significantly interrelated. Higher fluid attenuation inversion recovery weighted lesion volume was significantly associated with increased reports of pain (p = 0.01), lower physical quality of life (p < 0.0001), and lower cognitive performance (p = 0.001) in our cohort. CONCLUSIONS Assessment of pain and quality of life along with structural MRI highlights the importance of understanding structure-function relationships in MS and suggests that therapists should not only evaluate individuals for cognition and quality of life, but should consider rehabilitation goals that target these areas.
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Affiliation(s)
- Nora E Fritz
- Department of Physical Therapy, Wayne State University, Detroit, MI, USA.,Motion Analysis Laboratory, Kennedy Krieger Institute, Baltimore, MD, USA.,Johns Hopkins School of Medicine, Department of Physical Medicine & Rehabilitation, Baltimore, MD, USA
| | - Snehashis Roy
- Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, MD, USA
| | - Jennifer Keller
- Motion Analysis Laboratory, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jerry Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kathleen M Zackowski
- Motion Analysis Laboratory, Kennedy Krieger Institute, Baltimore, MD, USA.,Johns Hopkins School of Medicine, Department of Physical Medicine & Rehabilitation, Baltimore, MD, USA.,Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
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Carass A, Roy S, Jog A, Cuzzocreo JL, Magrath E, Gherman A, Button J, Nguyen J, Prados F, Sudre CH, Jorge Cardoso M, Cawley N, Ciccarelli O, Wheeler-Kingshott CAM, Ourselin S, Catanese L, Deshpande H, Maurel P, Commowick O, Barillot C, Tomas-Fernandez X, Warfield SK, Vaidya S, Chunduru A, Muthuganapathy R, Krishnamurthi G, Jesson A, Arbel T, Maier O, Handels H, Iheme LO, Unay D, Jain S, Sima DM, Smeets D, Ghafoorian M, Platel B, Birenbaum A, Greenspan H, Bazin PL, Calabresi PA, Crainiceanu CM, Ellingsen LM, Reich DS, Prince JL, Pham DL. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. Neuroimage 2017; 148:77-102. [PMID: 28087490 PMCID: PMC5344762 DOI: 10.1016/j.neuroimage.2016.12.064] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 11/15/2016] [Accepted: 12/19/2016] [Indexed: 01/12/2023] Open
Abstract
In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.
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Affiliation(s)
- Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Snehashis Roy
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA
| | - Amod Jog
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jennifer L Cuzzocreo
- Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Elizabeth Magrath
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA
| | - Adrian Gherman
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA
| | - Julia Button
- Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - James Nguyen
- Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Ferran Prados
- Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK; NMR Research Unit, UCL Institute of Neurology, WC1N 3BG London, UK
| | - Carole H Sudre
- Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK
| | - Manuel Jorge Cardoso
- Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK; Dementia Research Centre, UCL Institute of Neurology, WC1N 3BG London, UK
| | - Niamh Cawley
- NMR Research Unit, UCL Institute of Neurology, WC1N 3BG London, UK
| | - Olga Ciccarelli
- NMR Research Unit, UCL Institute of Neurology, WC1N 3BG London, UK
| | | | - Sébastien Ourselin
- Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK; Dementia Research Centre, UCL Institute of Neurology, WC1N 3BG London, UK
| | - Laurence Catanese
- VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France
| | | | - Pierre Maurel
- VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France
| | - Olivier Commowick
- VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France
| | - Christian Barillot
- VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France
| | - Xavier Tomas-Fernandez
- Computational Radiology Laboratory, Boston Childrens Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Childrens Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Suthirth Vaidya
- Biomedical Imaging Lab, Department of Engineering Design, Indian Institute of Technology, Chennai 600036, India
| | - Abhijith Chunduru
- Biomedical Imaging Lab, Department of Engineering Design, Indian Institute of Technology, Chennai 600036, India
| | - Ramanathan Muthuganapathy
- Biomedical Imaging Lab, Department of Engineering Design, Indian Institute of Technology, Chennai 600036, India
| | - Ganapathy Krishnamurthi
- Biomedical Imaging Lab, Department of Engineering Design, Indian Institute of Technology, Chennai 600036, India
| | - Andrew Jesson
- Centre For Intelligent Machines, McGill University, Montréal, QC H3A 0E9, Canada
| | - Tal Arbel
- Centre For Intelligent Machines, McGill University, Montréal, QC H3A 0E9, Canada
| | - Oskar Maier
- Institute of Medical Informatics, University of Lübeck, 23538 Lübeck, Germany
| | - Heinz Handels
- Institute of Medical Informatics, University of Lübeck, 23538 Lübeck, Germany
| | - Leonardo O Iheme
- Bahçeşehir University, Faculty of Engineering and Natural Sciences, 34349 Beşiktaş, Turkey
| | - Devrim Unay
- Bahçeşehir University, Faculty of Engineering and Natural Sciences, 34349 Beşiktaş, Turkey
| | | | | | | | - Mohsen Ghafoorian
- Institute for Computing and Information Sciences, Radboud University, 6525 HP Nijmegen, Netherlands
| | - Bram Platel
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6525 GA Nijmegen, Netherlands
| | - Ariel Birenbaum
- Department of Electrical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel
| | - Hayit Greenspan
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel
| | - Pierre-Louis Bazin
- Department of Neurophysics, Max Planck Institute, 04103 Leipzig, Germany
| | - Peter A Calabresi
- Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | | | - Lotta M Ellingsen
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Electrical and Computer Engineering, University of Iceland, 107 Reykjavík, Iceland
| | - Daniel S Reich
- Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA; Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Dzung L Pham
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA
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Roy S, Butman JA, Pham DL. Robust skull stripping using multiple MR image contrasts insensitive to pathology. Neuroimage 2017; 146:132-147. [PMID: 27864083 PMCID: PMC5321800 DOI: 10.1016/j.neuroimage.2016.11.017] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 10/31/2016] [Accepted: 11/04/2016] [Indexed: 01/18/2023] Open
Abstract
Automatic skull-stripping or brain extraction of magnetic resonance (MR) images is often a fundamental step in many neuroimage processing pipelines. The accuracy of subsequent image processing relies on the accuracy of the skull-stripping. Although many automated stripping methods have been proposed in the past, it is still an active area of research particularly in the context of brain pathology. Most stripping methods are validated on T1-w MR images of normal brains, especially because high resolution T1-w sequences are widely acquired and ground truth manual brain mask segmentations are publicly available for normal brains. However, different MR acquisition protocols can provide complementary information about the brain tissues, which can be exploited for better distinction between brain, cerebrospinal fluid, and unwanted tissues such as skull, dura, marrow, or fat. This is especially true in the presence of pathology, where hemorrhages or other types of lesions can have similar intensities as skull in a T1-w image. In this paper, we propose a sparse patch based Multi-cONtrast brain STRipping method (MONSTR),2 where non-local patch information from one or more atlases, which contain multiple MR sequences and reference delineations of brain masks, are combined to generate a target brain mask. We compared MONSTR with four state-of-the-art, publicly available methods: BEaST, SPECTRE, ROBEX, and OptiBET. We evaluated the performance of these methods on 6 datasets consisting of both healthy subjects and patients with various pathologies. Three datasets (ADNI, MRBrainS, NAMIC) are publicly available, consisting of 44 healthy volunteers and 10 patients with schizophrenia. Other three in-house datasets, comprising 87 subjects in total, consisted of patients with mild to severe traumatic brain injury, brain tumors, and various movement disorders. A combination of T1-w, T2-w were used to skull-strip these datasets. We show significant improvement in stripping over the competing methods on both healthy and pathological brains. We also show that our multi-contrast framework is robust and maintains accurate performance across different types of acquisitions and scanners, even when using normal brains as atlases to strip pathological brains, demonstrating that our algorithm is applicable even when reference segmentations of pathological brains are not available to be used as atlases.
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Affiliation(s)
- Snehashis Roy
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, United States.
| | - John A Butman
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, United States; Diagnostic Radiology Department, National Institute of Health, United States
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, United States
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Automated tissue segmentation of MR brain images in the presence of white matter lesions. Med Image Anal 2017; 35:446-457. [DOI: 10.1016/j.media.2016.08.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 08/27/2016] [Accepted: 08/29/2016] [Indexed: 12/15/2022]
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Patch Based Synthesis of Whole Head MR Images: Application to EPI Distortion Correction. ACTA ACUST UNITED AC 2016; 9968:146-156. [PMID: 28367541 DOI: 10.1007/978-3-319-46630-9_15] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Different magnetic resonance imaging pulse sequences are used to generate image contrasts based on physical properties of tissues, which provide different and often complementary information about them. Therefore multiple image contrasts are useful for multimodal analysis of medical images. Often, medical image processing algorithms are optimized for particular image contrasts. If a desirable contrast is unavailable, contrast synthesis (or modality synthesis) methods try to "synthesize" the unavailable constrasts from the available ones. Most of the recent image synthesis methods generate synthetic brain images, while whole head magnetic resonance (MR) images can also be useful for many applications. We propose an atlas based patch matching algorithm to synthesize T2-w whole head (including brain, skull, eyes etc) images from T1-w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to in-homogeneous B0 magnetic field are often corrected by non-linearly registering the corresponding b = 0 image with zero diffusion gradient to an undistorted T2-w image. We show that our synthetic T2-w images can be used as a template in absence of a real T2-w image. Our patch based method requires multiple atlases with T1 and T2 to be registeLowRes to a given target T1. Then for every patch on the target, multiple similar looking matching patches are found on the atlas T1 images and corresponding patches on the atlas T2 images are combined to generate a synthetic T2 of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized T2 images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.
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Shen D, Zhang D, Young A, Parvin B. Machine Learning and Data Mining in Medical Imaging. IEEE J Biomed Health Inform 2016; 19:1587-8. [PMID: 26574616 DOI: 10.1109/jbhi.2015.2444011] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Ellingsen LM, Roy S, Carass A, Blitz AM, Pham DL, Prince JL. Segmentation and labeling of the ventricular system in normal pressure hydrocephalus using patch-based tissue classification and multi-atlas labeling. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9784. [PMID: 27199501 DOI: 10.1117/12.2216511] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Normal pressure hydrocephalus (NPH) affects older adults and is thought to be caused by obstruction of the normal flow of cerebrospinal fluid (CSF). NPH typically presents with cognitive impairment, gait dysfunction, and urinary incontinence, and may account for more than five percent of all cases of dementia. Unlike most other causes of dementia, NPH can potentially be treated and the neurological dysfunction reversed by shunt surgery or endoscopic third ventriculostomy (ETV), which drain excess CSF. However, a major diagnostic challenge remains to robustly identify shunt-responsive NPH patients from patients with enlarged ventricles due to other neurodegenerative diseases. Currently, radiologists grade the severity of NPH by detailed examination and measurement of the ventricles based on stacks of 2D magnetic resonance images (MRIs). Here we propose a new method to automatically segment and label different compartments of the ventricles in NPH patients from MRIs. While this task has been achieved in healthy subjects, the ventricles in NPH are both enlarged and deformed, causing current algorithms to fail. Here we combine a patch-based tissue classification method with a registration-based multi-atlas labeling method to generate a novel algorithm that labels the lateral, third, and fourth ventricles in subjects with ventriculomegaly. The method is also applicable to other neurodegenerative diseases such as Alzheimer's disease; a condition considered in the differential diagnosis of NPH. Comparison with state of the art segmentation techniques demonstrate substantial improvements in labeling the enlarged ventricles, indicating that this strategy may be a viable option for the diagnosis and characterization of NPH.
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Affiliation(s)
- Lotta M Ellingsen
- Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Snehashis Roy
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ari M Blitz
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Dzung L Pham
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
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