Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis

A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach

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.

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

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.

Content-Based Medical Image Retrieval and Intelligent Interactive Visual Browser for Medical Education, Research and Care

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.

Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup

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.

OtoMatch: Content-based eardrum image retrieval using deep learning

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.

Using content-based image retrieval of dermoscopic images for interpretation and education: A pilot study

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.

Structural neuroimaging of the altered brain stemming from pediatric and adolescent hearing loss-Scientific and clinical challenges

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.

Identifying patients with neuronal intranuclear inclusion disease in Singapore using characteristic diffusion-weighted MR images

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.

Regional Shape Abnormalities in Thalamus and Verbal Memory Impairment After Subcortical Infarction

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.

Radiological images and machine learning: Trends, perspectives, and prospects

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.

An overview of deep learning in medical imaging focusing on MRI

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.

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI-Application in Premanifest Huntington's Disease

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.

Overview on subjective similarity of images for content-based medical image retrieval

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.

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

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.

Baby brain atlases

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.

Brain MRI Pattern Recognition Translated to Clinical Scenarios

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.

Mapping the order and pattern of brain structural MRI changes using change-point analysis in premanifest Huntington's disease

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.

Structural signature of classical versus late-onset friedreich's ataxia by Multimodality brain MRI

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.

Linking white matter and deep gray matter alterations in premanifest Huntington disease

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.

Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI

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.