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Reena MR, Ameer PM. A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach. Comput Biol Med 2022; 145:105463. [PMID: 35421794 DOI: 10.1016/j.compbiomed.2022.105463] [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: 10/25/2021] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 12/01/2022]
Abstract
Lymphomas, or cancers of the lymphatic system, account for around half of all blood cancers diagnosed each year. Lymphoma is a condition that is difficult to diagnose, and accurate diagnosis is critical for effective treatment. Manual microscopic analysis of blood cells requires the involvement of medical experts, whose precision is dependent on their abilities, and it takes time. This paper describes a content-based image retrieval system that uses deep learning-based feature extraction and a traditional learning method for feature reduction to retrieve similar images from a database to aid early/initial lymphoma diagnosis. The proposed algorithm employs a pre-trained network called ResNet-101 to extract image features required to distinguish four types of cells: lymphoma cells, blasts, lymphocytes, and other cells. The issue of class imbalance is resolved by over-sampling the training data followed by data augmentation. Deep learning features are extracted using the activations of the feature layer in the pre-trained net, then dimensionality reduction techniques are used to select discriminant features for the image retrieval system. Euclidean distance is used as the similarity measure to retrieve similar images from the database. The experimentation uses a microscopic blood image dataset with 1673 leukocytes of the categories blasts, lymphoma, lymphocytes, and other cells. The proposed algorithm achieves 98.74% precision in lymphoma cell classification and 99.22% precision @10 for lymphoma cell image retrieval. Experimental findings confirm our approach's practicability and effectiveness. Extended studies endorse the idea of using the prescribed system in actual medical applications, helping doctors diagnose lymphoma, dramatically reducing human resource requirements.
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Affiliation(s)
- M Roy Reena
- Department of Electronics and Communication Engineering, National Institute of Technology, Calicut, India.
| | - P M Ameer
- Department of Electronics and Communication Engineering, National Institute of Technology, Calicut, India
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2
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Bento M, Fantini I, Park J, Rittner L, Frayne R. Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets. Front Neuroinform 2022; 15:805669. [PMID: 35126080 PMCID: PMC8811356 DOI: 10.3389/fninf.2021.805669] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/27/2021] [Indexed: 12/22/2022] Open
Abstract
Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.
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Affiliation(s)
- Mariana Bento
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- *Correspondence: Mariana Bento
| | - Irene Fantini
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Justin Park
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- Radiology and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Leticia Rittner
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Richard Frayne
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- Radiology and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
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3
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Sotomayor CG, Mendoza M, Castañeda V, Farías H, Molina G, Pereira G, Härtel S, Solar M, Araya M. Content-Based Medical Image Retrieval and Intelligent Interactive Visual Browser for Medical Education, Research and Care. Diagnostics (Basel) 2021; 11:diagnostics11081470. [PMID: 34441404 PMCID: PMC8392084 DOI: 10.3390/diagnostics11081470] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/03/2021] [Accepted: 08/09/2021] [Indexed: 01/17/2023] Open
Abstract
Medical imaging is essential nowadays throughout medical education, research, and care. Accordingly, international efforts have been made to set large-scale image repositories for these purposes. Yet, to date, browsing of large-scale medical image repositories has been troublesome, time-consuming, and generally limited by text search engines. A paradigm shift, by means of a query-by-example search engine, would alleviate these constraints and beneficially impact several practical demands throughout the medical field. The current project aims to address this gap in medical imaging consumption by developing a content-based image retrieval (CBIR) system, which combines two image processing architectures based on deep learning. Furthermore, a first-of-its-kind intelligent visual browser was designed that interactively displays a set of imaging examinations with similar visual content on a similarity map, making it possible to search for and efficiently navigate through a large-scale medical imaging repository, even if it has been set with incomplete and curated metadata. Users may, likewise, provide text keywords, in which case the system performs a content- and metadata-based search. The system was fashioned with an anonymizer service and designed to be fully interoperable according to international standards, to stimulate its integration within electronic healthcare systems and its adoption for medical education, research and care. Professionals of the healthcare sector, by means of a self-administered questionnaire, underscored that this CBIR system and intelligent interactive visual browser would be highly useful for these purposes. Further studies are warranted to complete a comprehensive assessment of the performance of the system through case description and protocolized evaluations by medical imaging specialists.
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Affiliation(s)
- Camilo G. Sotomayor
- Radiology Department, Clinical Hospital University of Chile, University of Chile, Santiago 8380453, Chile; (C.G.S.); (G.P.)
- Center for Medical Informatics and Telemedicine, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile; (V.C.); (S.H.)
- Department of Electronic Engineering, Federico Santa Maria Technical University, Valparaíso 2340000, Chile
| | - Marcelo Mendoza
- Department of Informatics, Federico Santa Maria Technical University, Santiago 8380453, Chile; (M.M.); (H.F.); (G.M.); (M.S.)
| | - Víctor Castañeda
- Center for Medical Informatics and Telemedicine, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile; (V.C.); (S.H.)
- Department of Medical Technology, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Humberto Farías
- Department of Informatics, Federico Santa Maria Technical University, Santiago 8380453, Chile; (M.M.); (H.F.); (G.M.); (M.S.)
| | - Gabriel Molina
- Department of Informatics, Federico Santa Maria Technical University, Santiago 8380453, Chile; (M.M.); (H.F.); (G.M.); (M.S.)
| | - Gonzalo Pereira
- Radiology Department, Clinical Hospital University of Chile, University of Chile, Santiago 8380453, Chile; (C.G.S.); (G.P.)
| | - Steffen Härtel
- Center for Medical Informatics and Telemedicine, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile; (V.C.); (S.H.)
| | - Mauricio Solar
- Department of Informatics, Federico Santa Maria Technical University, Santiago 8380453, Chile; (M.M.); (H.F.); (G.M.); (M.S.)
| | - Mauricio Araya
- Department of Electronic Engineering, Federico Santa Maria Technical University, Valparaíso 2340000, Chile
- Correspondence:
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Faria AV, Zhao Y, Ye C, Hsu J, Yang K, Cifuentes E, Wang L, Mori S, Miller M, Caffo B, Sawa A. Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup. Hum Brain Mapp 2020; 42:1034-1053. [PMID: 33377594 PMCID: PMC7856640 DOI: 10.1002/hbm.25276] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/29/2020] [Accepted: 10/18/2020] [Indexed: 02/06/2023] Open
Abstract
Multi‐institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure‐based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI—rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure‐based analysis showed widespread DTI abnormalities in FEP and rs‐fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof‐of‐concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub‐groups.
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Affiliation(s)
- Andreia V Faria
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yi Zhao
- Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, Indiana, USA
| | - Chenfei Ye
- Department of Electronics and Information, Harbin Institute of Technology Shenzhen Graduate School, Guangdong, China
| | - Johnny Hsu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kun Yang
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Elizabeth Cifuentes
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences and Radiology, Northwestern University, Evanston, Illinois, USA
| | - Susumu Mori
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael Miller
- Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA
| | - Brian Caffo
- Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Akira Sawa
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA.,Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Mental Health, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
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Camalan S, Niazi MKK, Moberly AC, Teknos T, Essig G, Elmaraghy C, Taj-Schaal N, Gurcan MN. OtoMatch: Content-based eardrum image retrieval using deep learning. PLoS One 2020; 15:e0232776. [PMID: 32413096 PMCID: PMC7228122 DOI: 10.1371/journal.pone.0232776] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 04/21/2020] [Indexed: 12/29/2022] Open
Abstract
Acute infections of the middle ear are the most commonly treated childhood diseases. Because complications affect children's language learning and cognitive processes, it is essential to diagnose these diseases in a timely and accurate manner. The prevailing literature suggests that it is difficult to accurately diagnose these infections, even for experienced ear, nose, and throat (ENT) physicians. Advanced care practitioners (e.g., nurse practitioners, physician assistants) serve as first-line providers in many primary care settings and may benefit from additional guidance to appropriately determine the diagnosis and treatment of ear diseases. For this purpose, we designed a content-based image retrieval (CBIR) system (called OtoMatch) for normal, middle ear effusion, and tympanostomy tube conditions, operating on eardrum images captured with a digital otoscope. We present a method that enables the conversion of any convolutional neural network (trained for classification) into an image retrieval model. As a proof of concept, we converted a pre-trained deep learning model into an image retrieval system. We accomplished this by changing the fully connected layers into lookup tables. A database of 454 labeled eardrum images (179 normal, 179 effusion, and 96 tube cases) was used to train and test the system. On a 10-fold cross validation, the proposed method resulted in an average accuracy of 80.58% (SD 5.37%), and maximum F1 score of 0.90 while retrieving the most similar image from the database. These are promising results for the first study to demonstrate the feasibility of developing a CBIR system for eardrum images using the newly proposed methodology.
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Affiliation(s)
- Seda Camalan
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Muhammad Khalid Khan Niazi
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Aaron C. Moberly
- Department of Otolaryngology, Ohio State University, Columbus, Ohio, United States of America
| | - Theodoros Teknos
- UH Seidman Cancer Center, Cleveland, Ohio, United States of America
| | - Garth Essig
- Department of Otolaryngology, Ohio State University, Columbus, Ohio, United States of America
| | - Charles Elmaraghy
- Department of Otolaryngology, Ohio State University, Columbus, Ohio, United States of America
| | - Nazhat Taj-Schaal
- Department of Internal Medicine, Ohio State University, Columbus, Ohio, United States of America
| | - Metin N. Gurcan
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
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6
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Ratnanather JT. Structural neuroimaging of the altered brain stemming from pediatric and adolescent hearing loss-Scientific and clinical challenges. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1469. [PMID: 31802640 PMCID: PMC7307271 DOI: 10.1002/wsbm.1469] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 10/01/2019] [Accepted: 10/13/2019] [Indexed: 12/20/2022]
Abstract
There has been a spurt in structural neuroimaging studies of the effect of hearing loss on the brain. Specifically, magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) technologies provide an opportunity to quantify changes in gray and white matter structures at the macroscopic scale. To date, there have been 32 MRI and 23 DTI studies that have analyzed structural differences accruing from pre- or peri-lingual pediatric hearing loss with congenital or early onset etiology and postlingual hearing loss in pre-to-late adolescence. Additionally, there have been 15 prospective clinical structural neuroimaging studies of children and adolescents being evaluated for cochlear implants. The results of the 70 studies are summarized in two figures and three tables. Plastic changes in the brain are seen to be multifocal rather than diffuse, that is, differences are consistent across regions implicated in the hearing, speech and language networks regardless of modes of communication and amplification. Structures in that play an important role in cognition are affected to a lesser extent. A limitation of these studies is the emphasis on volumetric measures and on homogeneous groups of subjects with hearing loss. It is suggested that additional measures of morphometry and connectivity could contribute to a greater understanding of the effect of hearing loss on the brain. Then an interpretation of the observed macroscopic structural differences is given. This is followed by discussion of how structural imaging can be combined with functional imaging to provide biomarkers for longitudinal tracking of amplification. This article is categorized under: Developmental Biology > Developmental Processes in Health and Disease Translational, Genomic, and Systems Medicine > Translational Medicine Laboratory Methods and Technologies > Imaging.
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Affiliation(s)
- J. Tilak Ratnanather
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
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7
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Sadeghi M, Chilana P, Yap J, Tschandl P, Atkins MS. Using content-based image retrieval of dermoscopic images for interpretation and education: A pilot study. Skin Res Technol 2019; 26:503-512. [PMID: 31845429 DOI: 10.1111/srt.12822] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 11/09/2019] [Indexed: 12/26/2022]
Abstract
BACKGROUND Dermoscopic content-based image retrieval (CBIR) systems provide a set of visually similar dermoscopic (magnified and illuminated) skin images with a pathology-confirmed diagnosis for a given dermoscopic query image of a skin lesion. Although recent advances in machine learning have spurred novel CBIR algorithms, we have few insights into how end users interact with CBIRs and to what extent CBIRs can be useful for education and image interpretation. MATERIALS AND METHODS We developed an interactive user interface for a CBIR system with dermoscopic images as a decision support tool and investigated users' interactions and decisions with the system. We performed a pilot experiment with 14 non-medically trained users for a given set of annotated dermoscopic images. RESULTS Our pilot showed that the number of correct classifications and users' confidence levels significantly increased with the CBIR interface compared with a non-CBIR interface, although the timing also increased significantly. The users found the CBIR interface of high educational value, engaging and easy to use. CONCLUSION Overall, users became more accurate, found the CBIR approach provided a useful decision aid, and had educational value for learning about skin conditions.
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Affiliation(s)
- Mahya Sadeghi
- School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Parmit Chilana
- School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Jordan Yap
- MetaOptima Technology Inc., Vancouver, BC, Canada
| | - Philipp Tschandl
- School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.,Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - M Stella Atkins
- School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.,School of Dermatology and Skin Science, University of British Columbia, Vancouver, BC, Canada
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Identifying patients with neuronal intranuclear inclusion disease in Singapore using characteristic diffusion-weighted MR images. Neuroradiology 2019; 61:1281-1290. [PMID: 31292692 DOI: 10.1007/s00234-019-02257-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 07/02/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE Adult-onset neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder described mainly in the Japanese population, with characteristic DWI abnormalities at the junction between gray and white matter. We identify possible cases of NIID in the picture archive and communication system (PACS) of a tertiary neurological referral hospital in Singapore and describe their radiological features. METHODS The neuroradiology imaging database was reviewed using keyword search of radiological reports to identify patients who had "subcortical U fibre" abnormalities on DWI. MRI were retrospectively reviewed, and those fulfilling inclusion criteria were invited for skin biopsy to detect nuclear inclusions by light and electron microscopy. RESULTS Twelve Chinese patients (nine female; median age 70.5 years) were enrolled. Seven patients were being assessed for dementia and five for other neurological indications. In all patients, DWI showed distinctive subcortical high signal with increased average apparent diffusion coefficient (ADC), involving frontal, parietal, and temporal more than occipital lobes; the corpus callosum and external capsule were affected in some patients. On T2-weighted images, cerebral and cerebellar atrophy and white matter hyperintensity of Fazekas grade 2 and above were seen in all patients. Three patients underwent skin biopsy; all were positive for intranuclear hyaline inclusion bodies on either p62 staining or electron microscopy, which are pathognomonic for NIID. CONCLUSION Previously undiagnosed patients with NIID can be identified by searching for abnormalities at the junction between gray and white matter on DWI in PACS and subsequently confirmed by skin biopsy. Radiologists should recognize the distinctive neuroimaging pattern of this dementing disease.
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Liu G, Tan X, Dang C, Tan S, Xing S, Huang N, Peng K, Xie C, Tang X, Zeng J. Regional Shape Abnormalities in Thalamus and Verbal Memory Impairment After Subcortical Infarction. Neurorehabil Neural Repair 2019; 33:476-485. [PMID: 31081462 DOI: 10.1177/1545968319846121] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background. Subcortical infarcts can result in verbal memory impairment, but the potential underlying mechanisms remain unknown. Objective. We investigated the spatiotemporal deterioration patterns of brain structures in patients with subcortical infarction and identified the regions that contributed to verbal memory impairment. Methods. Cognitive assessment and structural magnetic resonance imaging were performed 1, 4, and 12 weeks after stroke onset in 28 left-hemisphere and 22 right-hemisphere stroke patients with subcortical infarction. Whole-brain volumetric analysis combined with a further-refined shape analysis was conducted to analyze longitudinal morphometric changes in brain structures and their relationship to verbal memory performance. Results. Between weeks 1 and 12, significant volume decreases in the ipsilesional basal ganglia, inferior white matter, and thalamus were found in the left-hemisphere stroke group. Among those 3 structures, only the change rate of the thalamus volume was significantly correlated with that in immediate recall. For the right-hemisphere stroke group, only the ipsilesional basal ganglia survived the week 1 to week 12 group comparison, but its change rate was not significantly correlated with the verbal memory change rate. Shape analysis of the thalamus revealed atrophies of the ipsilesional thalamic subregions connected to the prefrontal, temporal, and premotor cortices in the left-hemisphere stroke group and positive correlations between the rates of those atrophies and the change rate in immediate recall. Conclusions. Secondary damage to the thalamus, especially to the left subregions connected to specific cortices, may be associated with early verbal memory impairment following an acute subcortical infarct.
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Affiliation(s)
- Gang Liu
- 1 The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xiaoqing Tan
- 2 Southern University of Science and Technology, Shenzhen, Guangdong, China.,3 Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chao Dang
- 1 The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Shuangquan Tan
- 1 The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Shihui Xing
- 1 The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Nianwei Huang
- 2 Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Kangqiang Peng
- 4 Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
| | - Chuanmiao Xie
- 4 Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
| | - Xiaoying Tang
- 2 Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Jinsheng Zeng
- 1 The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
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10
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Zhang Z, Sejdić E. Radiological images and machine learning: Trends, perspectives, and prospects. Comput Biol Med 2019; 108:354-370. [PMID: 31054502 PMCID: PMC6531364 DOI: 10.1016/j.compbiomed.2019.02.017] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 02/19/2019] [Accepted: 02/19/2019] [Indexed: 01/18/2023]
Abstract
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.
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Affiliation(s)
- Zhenwei Zhang
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
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11
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Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 2018; 29:102-127. [PMID: 30553609 DOI: 10.1016/j.zemedi.2018.11.002] [Citation(s) in RCA: 705] [Impact Index Per Article: 117.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 11/19/2018] [Accepted: 11/21/2018] [Indexed: 02/06/2023]
Abstract
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.
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Affiliation(s)
- Alexander Selvikvåg Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Norway; Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Norway.
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Norway; Neuroinformatics and Image Analysis Laboratory, Department of Biomedicine, University of Bergen, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Norway.
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12
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Wu D, Faria AV, Younes L, Ross CA, Mori S, Miller MI. Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI-Application in Premanifest Huntington's Disease. J Vis Exp 2018. [PMID: 29939188 DOI: 10.3791/57256] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington's disease (HD), regional brain atrophy begins many years prior to the motor onset (during the "premanifest" period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, "MRICloud", which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the "change-point", with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.
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Affiliation(s)
- Dan Wu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine;
| | - Andreia V Faria
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine
| | - Laurent Younes
- Center for Imaging Science, Johns Hopkins University; Institute for Computational Medicine, Johns Hopkins University; Department of Applied Mathematics and Statistics, Johns Hopkins University
| | - Christopher A Ross
- Division of Neurobiology, Departments of Psychiatry, Neurology, Neuroscience and Pharmacology, and Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University; Institute for Computational Medicine, Johns Hopkins University; Department of Biomedical Engineering, Johns Hopkins University
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Muramatsu C. Overview on subjective similarity of images for content-based medical image retrieval. Radiol Phys Technol 2018; 11:109-124. [PMID: 29740749 DOI: 10.1007/s12194-018-0461-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 04/28/2018] [Indexed: 12/18/2022]
Abstract
Computer-aided diagnosis systems for assisting the classification of various diseases have the potential to improve radiologists' diagnostic accuracy and efficiency, as reported in several studies. Conventional systems generally provide the probabilities of disease types in terms of numerical values, a method that may not be efficient for radiologists who are trained by reading a large number of images. Presentation of reference images similar to those of a new case being diagnosed can supplement the probability outputs based on computerized analysis as an intuitive guide, and it can assist radiologists in their diagnosis, reporting, and treatment planning. Many studies on content-based medical image retrievals have been reported on. For retrieval of perceptually similar and diagnostically relevant images, incorporation of perceptual similarity data by radiologists has been suggested. In this paper, studies on image retrieval methods are reviewed with a special focus on quantification, utilization, and the evaluation of subjective similarities between pairs of images.
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Affiliation(s)
- Chisako Muramatsu
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
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14
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Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments. SENSORS 2018; 18:s18051329. [PMID: 29693590 PMCID: PMC5982789 DOI: 10.3390/s18051329] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 04/18/2018] [Accepted: 04/21/2018] [Indexed: 01/08/2023]
Abstract
Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo. To describe the textile regions, we demonstrated that the combination of HOG and HCLOSIB is the best option for our proposal when using the correlation distance to match the query textile patch with the candidate regions. Furthermore, we introduce a new dataset, TextilTube, which comprises a total of 1913 textile regions labelled within 67 classes. We yielded 84.94% of success in the 40 nearest coincidences and 37.44% of precision taking into account just the first coincidence, which outperforms the current deep learning methods evaluated. Experimental results show that this pipeline can be used to set up an effective textile based image retrieval system in indoor environments.
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15
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Oishi K, Chang L, Huang H. Baby brain atlases. Neuroimage 2018; 185:865-880. [PMID: 29625234 DOI: 10.1016/j.neuroimage.2018.04.003] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 02/27/2018] [Accepted: 04/02/2018] [Indexed: 01/23/2023] Open
Abstract
The baby brain is constantly changing due to its active neurodevelopment, and research into the baby brain is one of the frontiers in neuroscience. To help guide neuroscientists and clinicians in their investigation of this frontier, maps of the baby brain, which contain a priori knowledge about neurodevelopment and anatomy, are essential. "Brain atlas" in this review refers to a 3D-brain image with a set of reference labels, such as a parcellation map, as the anatomical reference that guides the mapping of the brain. Recent advancements in scanners, sequences, and motion control methodologies enable the creation of various types of high-resolution baby brain atlases. What is becoming clear is that one atlas is not sufficient to characterize the existing knowledge about the anatomical variations, disease-related anatomical alterations, and the variations in time-dependent changes. In this review, the types and roles of the human baby brain MRI atlases that are currently available are described and discussed, and future directions in the field of developmental neuroscience and its clinical applications are proposed. The potential use of disease-based atlases to characterize clinically relevant information, such as clinical labels, in addition to conventional anatomical labels, is also discussed.
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Affiliation(s)
- Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Linda Chang
- Departments of Diagnostic Radiology and Nuclear Medicine, and Neurology, University of Maryland School of Medicine, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Hao Huang
- Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
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16
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Faria AV, Liang Z, Miller MI, Mori S. Brain MRI Pattern Recognition Translated to Clinical Scenarios. Front Neurosci 2017; 11:578. [PMID: 29104527 PMCID: PMC5655969 DOI: 10.3389/fnins.2017.00578] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 10/02/2017] [Indexed: 12/27/2022] Open
Abstract
We explored the performance of structure-based computational analysis in four neurodegenerative conditions [Ataxia (AT, n = 16), Huntington's Disease (HD, n = 52), Alzheimer's Disease (AD, n = 66), and Primary Progressive Aphasia (PPA, n = 50)], all characterized by brain atrophy. The independent variables were the volumes of 283 anatomical areas, derived from automated segmentation of T1-high resolution brain MRIs. The segmentation based volumetric quantification reduces image dimensionality from the voxel level [on the order of O(106)] to anatomical structures [O(102)] for subsequent statistical analysis. We evaluated the effectiveness of this approach on extracting anatomical features, already described by human experience and a priori biological knowledge, in specific scenarios: (1) when pathologies were relatively homogeneous, with evident image alterations (e.g., AT); (2) when the time course was highly correlated with the anatomical changes (e.g., HD), an analogy for prediction; (3) when the pathology embraced heterogeneous phenotypes (e.g., AD) so the classification was less efficient but, in compensation, anatomical and clinical information were less redundant; and (4) when the entity was composed of multiple subgroups that had some degree of anatomical representation (e.g., PPA), showing the potential of this method for the clustering of more homogeneous phenotypes that can be of clinical importance. Using the structure-based quantification and simple linear classifiers (partial least square), we achieve 87.5 and 73% of accuracy on differentiating AT and pre-symptomatic HD patents from controls, respectively. More importantly, the anatomical features automatically revealed by the classifiers agreed with the patterns previously described on these pathologies. The accuracy was lower (68%) on differentiating AD from controls, as AD does not display a clear anatomical phenotype. On the other hand, the method identified PPA clinical phenotypes and their respective anatomical signatures. Although most of the data are presented here as proof of concept in simulated clinical scenarios, structure-based analysis was potentially effective in characterizing phenotypes, retrieving relevant anatomical features, predicting prognosis, and aiding diagnosis, with the advantage of being easily translatable to clinics and understandable biologically.
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Affiliation(s)
- Andreia V Faria
- Department of Radiology, Johns Hopkins University, Baltimore, MD, United States
| | - Zifei Liang
- Department of Radiology, New York University, New York, NY, United States
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Susumu Mori
- Department of Radiology, Johns Hopkins University, Baltimore, MD, United States
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Wu D, Faria AV, Younes L, Mori S, Brown T, Johnson H, Paulsen JS, Ross CA, Miller MI. Mapping the order and pattern of brain structural MRI changes using change-point analysis in premanifest Huntington's disease. Hum Brain Mapp 2017; 38:5035-5050. [PMID: 28657159 PMCID: PMC5766002 DOI: 10.1002/hbm.23713] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 06/12/2017] [Accepted: 06/19/2017] [Indexed: 02/02/2023] Open
Abstract
Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder that progressively affects motor, cognitive, and emotional functions. Structural MRI studies have demonstrated brain atrophy beginning many years prior to clinical onset ("premanifest" period), but the order and pattern of brain structural changes have not been fully characterized. In this study, we investigated brain regional volumes and diffusion tensor imaging (DTI) measurements in premanifest HD, and we aim to determine (1) the extent of MRI changes in a large number of structures across the brain by atlas-based analysis, and (2) the initiation points of structural MRI changes in these brain regions. We adopted a novel multivariate linear regression model to detect the inflection points at which the MRI changes begin (namely, "change-points"), with respect to the CAG-age product (CAP, an indicator of extent of exposure to the effects of CAG repeat expansion). We used approximately 300 T1-weighted and DTI data from premanifest HD and control subjects in the PREDICT-HD study, with atlas-based whole brain segmentation and change-point analysis. The results indicated a distinct topology of structural MRI changes: the change-points of the volumetric measurements suggested a central-to-peripheral pattern of atrophy from the striatum to the deep white matter; and the change points of DTI measurements indicated the earliest changes in mean diffusivity in the deep white matter and posterior white matter. While interpretation needs to be cautious given the cross-sectional nature of the data, these findings suggest a spatial and temporal pattern of spread of structural changes within the HD brain. Hum Brain Mapp 38:5035-5050, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Dan Wu
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMaryland
| | - Andreia V. Faria
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMaryland
| | - Laurent Younes
- Center for Imaging Science, Johns Hopkins UniversityBaltimoreMaryland
- Institute for Computational Medicine, Johns Hopkins UniversityBaltimoreMaryland
- Department of Applied Mathematics and StatisticsJohns Hopkins UniversityBaltimoreMaryland
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMaryland
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger InstituteBaltimoreMaryland
| | - Timothy Brown
- Center for Imaging Science, Johns Hopkins UniversityBaltimoreMaryland
| | - Hans Johnson
- Department of Electrical and Computer EngineeringUniversity of IowaIowa CityIowa
| | - Jane S. Paulsen
- Departments of Psychiatry, Neurology, Psychology and NeurosciencesUniversity of IowaIowa CityIowa
| | - Christopher A. Ross
- Division of Neurobiology, Departments of Psychiatry, Neurology, Neuroscience and Pharmacology, and Program in Cellular and Molecular MedicineJohns Hopkins University School of MedicineBaltimoreMaryland
| | - Michael I. Miller
- Center for Imaging Science, Johns Hopkins UniversityBaltimoreMaryland
- Institute for Computational Medicine, Johns Hopkins UniversityBaltimoreMaryland
- Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreMaryland
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18
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Rezende TJR, Martinez ARM, Faber I, Girotto K, Pedroso JL, Barsottini OG, Lopes-Cendes I, Cendes F, Faria AV, França MC. Structural signature of classical versus late-onset friedreich's ataxia by Multimodality brain MRI. Hum Brain Mapp 2017; 38:4157-4168. [PMID: 28543952 DOI: 10.1002/hbm.23655] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Revised: 05/08/2017] [Accepted: 05/11/2017] [Indexed: 01/08/2023] Open
Abstract
INTRODUCTION Friedreich's ataxia (FRDA) is the most common autosomal-recessive ataxia worldwide. It is characterized by early onset, sensory abnormalities, and slowly progressive ataxia. However, some individuals manifest the disease after the age of 25 years and are classified as late-onset FRDA (LOFA). Therefore, we propose a transversal multimodal MRI-based study to investigate which anatomical substrates are involved in classical (cFRDA) and LOFA. METHODS We enrolled 36 patients (13 with LOFA) and 29 healthy controls. All subjects underwent magnetic resonance imaging in a 3 T device; three-dimensional high-resolution T1-weighted images and diffusion tensor images were used to assess gray and white matter, respectively. We used T1 multiatlas approach to assess deep gray matter and cortical thickness measures to evaluate cerebral cortex and DTI multiatlas approach to assess white matter. All analyses were corrected for multiple comparisons. RESULTS Group comparison showed that both groups presented gray matter atrophy mostly in the motor cortex. Regarding white matter, we found abnormalities in the cerebellar peduncles, pyramidal tracts, midbrain, pons, and medulla oblongata for both groups, but the microstructural abnormalities in the cFRDA group were more widespread. In addition, we found that the corticospinal tract presented more severe microstructural damage in the LOFA group. Finally, the midbrain volume of the cFRDA, but not of the LOFA group, correlated with disease duration (R = -0.552, P = 0.012) and severity (R = -0.783, P < 0.001). CONCLUSION The cFRDA and LOFA groups have similar, but not identical neuroimaging damage pattern. These structural differences might help to explain the phenotypic variability observed in FRDA. Hum Brain Mapp 38:4157-4168, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Thiago Junqueira R Rezende
- Department of Neurology and Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil
| | - Alberto Rolim M Martinez
- Department of Neurology and Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil
| | - Ingrid Faber
- Department of Neurology and Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil
| | - Karen Girotto
- Department of Neurology and Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil
| | - José Luiz Pedroso
- Division of General Neurology and Ataxia Unit, Federal University of São Paulo, São Paulo, Sao Paulo, Brazil
| | - Orlando G Barsottini
- Division of General Neurology and Ataxia Unit, Federal University of São Paulo, São Paulo, Sao Paulo, Brazil
| | - Iscia Lopes-Cendes
- Department of Medical Genetics, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil
| | - Fernando Cendes
- Department of Neurology and Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil
| | - Andreia V Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Marcondes C França
- Department of Neurology and Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil
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Mori S, Wu D, Ceritoglu C, Li Y, Kolasny A, Vaillant MA, Faria AV, Oishi K, Miller MI. MRICloud: Delivering High-Throughput MRI Neuroinformatics as Cloud-Based Software as a Service. Comput Sci Eng 2016. [DOI: 10.1109/mcse.2016.93] [Citation(s) in RCA: 106] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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20
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Faria AV, Ratnanather JT, Tward DJ, Lee DS, van den Noort F, Wu D, Brown T, Johnson H, Paulsen JS, Ross CA, Younes L, Miller MI. Linking white matter and deep gray matter alterations in premanifest Huntington disease. Neuroimage Clin 2016; 11:450-460. [PMID: 27104139 PMCID: PMC4827723 DOI: 10.1016/j.nicl.2016.02.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 02/17/2016] [Accepted: 02/22/2016] [Indexed: 01/07/2023]
Abstract
Huntington disease (HD) is a fatal progressive neurodegenerative disorder for which only symptomatic treatment is available. A better understanding of the pathology, and identification of biomarkers will facilitate the development of disease-modifying treatments. HD is potentially a good model of a neurodegenerative disease for development of biomarkers because it is an autosomal-dominant disease with complete penetrance, caused by a single gene mutation, in which the neurodegenerative process can be assessed many years before onset of signs and symptoms of manifest disease. Previous MRI studies have detected abnormalities in gray and white matter starting in premanifest stages. However, the understanding of how these abnormalities are related, both in time and space, is still incomplete. In this study, we combined deep gray matter shape diffeomorphometry and white matter DTI analysis in order to provide a better mapping of pathology in the deep gray matter and subcortical white matter in premanifest HD. We used 296 MRI scans from the PREDICT-HD database. Atrophy in the deep gray matter, thalamus, hippocampus, and nucleus accumbens was analyzed by surface based morphometry, and while white matter abnormalities were analyzed in (i) regions of interest surrounding these structures, using (ii) tractography-based analysis, and using (iii) whole brain atlas-based analysis. We detected atrophy in the deep gray matter, particularly in putamen, from early premanifest stages. The atrophy was greater both in extent and effect size in cases with longer exposure to the effects of the CAG expansion mutation (as assessed by greater CAP-scores), and preceded detectible abnormalities in the white matter. Near the predicted onset of manifest HD, the MD increase was widespread, with highest indices in the deep and posterior white matter. This type of in-vivo macroscopic mapping of HD brain abnormalities can potentially indicate when and where therapeutics could be targeted to delay the onset or slow the disease progression.
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Affiliation(s)
- Andreia V Faria
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - J Tilak Ratnanather
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Daniel J Tward
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - David Soobin Lee
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Frieda van den Noort
- MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Dan Wu
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Timothy Brown
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA
| | - Hans Johnson
- Department of Psychiatry, The University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Jane S Paulsen
- Department of Psychiatry, The University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Christopher A Ross
- Division of Neurobiology, Department of Psychiatry, and Departments of Neurology, Neuroscience and Pharmacology, Johns Hopkins University, Baltimore, MD, USA
| | - Laurent Younes
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, MD, USA
| | - Michael I Miller
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
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Wu D, Ma T, Ceritoglu C, Li Y, Chotiyanonta J, Hou Z, Hsu J, Xu X, Brown T, Miller MI, Mori S. Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI. Neuroimage 2015; 125:120-130. [PMID: 26499813 DOI: 10.1016/j.neuroimage.2015.10.042] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2015] [Accepted: 10/17/2015] [Indexed: 01/07/2023] Open
Abstract
Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n=90), which cover ages 4-82years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which atlases that most closely match the subject's age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation.
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Affiliation(s)
- Dan Wu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ting Ma
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Can Ceritoglu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Yue Li
- AnatomyWorks, LLC, Baltimore, MD, USA
| | - Jill Chotiyanonta
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zhipeng Hou
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - John Hsu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Xin Xu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Timothy Brown
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Susumu Mori
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
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