LORIS: a web-based data management system for multi-center studies

Longitudinal Online Research and Imaging System (LORIS) is a modular and extensible web-based data management system that integrates all aspects of a multi-center study: from heterogeneous data acquisition (imaging, clinical, behavior, and genetics) to storage, processing, and ultimately dissemination. It provides a secure, user-friendly, and streamlined platform to automate the flow of clinical trials and complex multi-center studies. A subject-centric internal organization allows researchers to capture and subsequently extract all information, longitudinal or cross-sectional, from any subset of the study cohort. Extensive error-checking and quality control procedures, security, data management, data querying, and administrative functions provide LORIS with a triple capability (1) continuous project coordination and monitoring of data acquisition (2) data storage/cleaning/querying, (3) interface with arbitrary external data processing “pipelines.” LORIS is a complete solution that has been thoroughly tested through a full 10 year life cycle of a multi-center longitudinal project1 and is now supporting numerous international neurodevelopment and neurodegeneration research projects

Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice

The Dice score and Jaccard index are commonly used metrics for the evaluation of segmentation tasks in medical imaging. Convolutional neural networks trained for image segmentation tasks are usually optimized for (weighted) cross-entropy. This introduces an adverse discrepancy between the learning optimization objective (the loss) and the end target metric. Recent works in computer vision have proposed soft surrogates to alleviate this discrepancy and directly optimize the desired metric, either through relaxations (soft-Dice, soft-Jaccard) or submodular optimization (Lov\'asz-softmax). The aim of this study is two-fold. First, we investigate the theoretical differences in a risk minimization framework and question the existence of a weighted cross-entropy loss with weights theoretically optimized to surrogate Dice or Jaccard. Second, we empirically investigate the behavior of the aforementioned loss functions w.r.t. evaluation with Dice score and Jaccard index on five medical segmentation tasks. Through the application of relative approximation bounds, we show that all surrogates are equivalent up to a multiplicative factor, and that no optimal weighting of cross-entropy exists to approximate Dice or Jaccard measures. We validate these findings empirically and show that, while it is important to opt for one of the target metric surrogates rather than a cross-entropy-based loss, the choice of the surrogate does not make a statistical difference on a wide range of medical segmentation tasks.Comment: MICCAI 201

pBrain: A novel pipeline for Parkinson related brain structure segmentation

Parkinson is a very prevalent neurodegenerative disease impacting the life of millions of people worldwide. Although its cause remains unknown, its functional and structural analysis is fundamental to advance in the search of a cure or symptomatic treatment. The automatic segmentation of deep brain structures related to Parkinson`s disease could be beneficial for the follow up and treatment planning. Unfortunately, there is not broadly available segmentation software to automatically measure Parkinson related structures. In this paper, we present a novel pipeline to segment three deep brain structures related to Parkinson's disease (substantia nigra, subthalamic nucleus and red nucleus). The proposed method is based on the multi-atlas label fusion technology that works on standard and high-resolution T2-weighted images. The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The proposed method has been compared to other state-of-the-art methods showing competitive results in terms of accuracy and execution time

Assessing the performance of atlas-based prefrontal brain parcellation in an aging cohort

OBJECTIVE: It is unclear whether atlas-based parcellation is suitable in ageing cohorts because age-related brain changes confound the performance of automatic methods. We assessed atlas-based parcellation of the prefrontal lobe in an ageing population using visual assessment, volumetric and spatial concordance. METHODS: We used atlas-based approach to parcellate brain MR images of 90 non-demented healthy adults, aged 72.7±0.7yrs and assed performance. RESULTS: Volumetric assessment showed that both single- and multi-atlas-based methods performed acceptably (Intraclass correlation coefficient, ICC:0.74 to 0.76). Spatial overlap measurements showed that multi- (Dice Coefficient, DC:0.84) offered an improvement over the single- (DC:0.75 to 0.78) atlas approach. Visual assessment also showed that multi-atlas outperformed single-atlas, and identified an additional post-processing step of CSF removal, enhancing concordance (ICC:0.86, DC:0.89). CONCLUSIONS: Atlas-based parcellation performed reasonably well in the ageing population. Rigorous performance assessement aided method refinement, and emphasises the importance of age-matching and post-processing. Further work is required in more varied subjects

Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis

Focal lesions and brain atrophy are the most extensively studied aspects of multiple sclerosis (MS), but the image acquisition and analysis techniques used can be further improved, especially those for studying within-patient changes of lesion load and atrophy longitudinally. Improved accuracy and sensitivity will reduce the numbers of patients required to detect a given treatment effect in a trial, and ultimately, will allow reliable characterization of individual patients for personalized treatment. Based on open issues in the field of MS research, and the current state of the art in magnetic resonance image analysis methods for assessing brain lesion load and atrophy, this paper makes recommendations to improve these measures for longitudinal studies of MS. Briefly, they are (1) images should be acquired using 3D pulse sequences, with near-isotropic spatial resolution and multiple image contrasts to allow more comprehensive analyses of lesion load and atrophy, across timepoints. Image artifacts need special attention given their effects on image analysis results. (2) Automated image segmentation methods integrating the assessment of lesion load and atrophy are desirable. (3) A standard dataset with benchmark results should be set up to facilitate development, calibration, and objective evaluation of image analysis methods for MS

Providing traceability for neuroimaging analyses

IntroductionWith the increasingly digital nature of biomedical data and as the complexity of analyses in medical research increases, the need for accurate information capture, traceability and accessibility has become crucial to medical researchers in the pursuance of their research goals. Grid- or Cloud-based technologies, often based on so-called Service Oriented Architectures (SOA), are increasingly being seen as viable solutions for managing distributed data and algorithms in the bio-medical domain. For neuroscientific analyses, especially those centred on complex image analysis, traceability of processes and datasets is essential but up to now this has not been captured in a manner that facilitates collaborative study. Purpose and MethodFew examples exist, of deployed medical systems based on Grids that provide the traceability of research data needed to facilitate complex analyses and none have been evaluated in practice. Over the past decade, we have been working with mammographers, paediatricians and neuroscientists in three generations of projects to provide the data management and provenance services now required for 21st century medical research. This paper outlines the finding of a requirements study and a resulting system architecture for the production of services to support neuroscientific studies of biomarkers for Alzheimer’s Disease.ResultsThe paper proposes a software infrastructure and services that provide the foundation for such support. It introduces the use of the CRISTAL software to provide provenance management as one of a number of services delivered on a SOA, deployed to manage neuroimaging projects that have been studying biomarkers for Alzheimer’s disease. ConclusionsIn the neuGRID and N4U projects a Provenance Service has been delivered that captures and reconstructs the workflow information needed to facilitate researchers in conducting neuroimaging analyses. The software enables neuroscientists to track the evolution of workflows and datasets. It also tracks the outcomes of various analyses and provides provenance traceability throughout the lifecycle of their studies. As the Provenance Service has been designed to be generic it can be applied across the medical domain as a reusable tool for supporting medical researchers thus providing communities of researchers for the first time with the necessary tools to conduct widely distributed collaborative programmes of medical analysis

Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis

Current assessment of osteoarthritis (OA) is primary based on visual grading of joint space narrowing and osteophytes present on radiographs. The approach is observer-dependent, not sensitive enough for the detection of the early stages of OA and time consuming. A promising solution is through fractal analysis of trabecular bone (TB) textures on radiographs. The goal is to develop an automated decision support system for the detection and prediction of OA based on TB texture regions selected on knee and hand radiographs. In this review, we describe our progress towards this development which was conducted in five stages, i.e., (i) development of automated methods for the selection of TB texture regions on knee and hand radiographs (ii), development of fractal signature methods for TB texture analysis, (iii) applications of the methods in the analysis of x-ray images of knees and hands, (iv) development of TB texture classification system, and (v) development of ReadMyXray website for knee x-ray analysis. The results achieved so far are encouraging and it is hoped, that once the system is fully developed and evaluated, it will be used to aid medical practitioners in the decision-making, i.e., in designing OA preventative measures, treatments and monitoring the OA progression

Desperately seeking a cure: Treatment seeking and appraisal in irritable bowel syndrome

© 2018 The British Psychological Society Objectives: Irritable bowel syndrome (IBS) is common and adversely affects patients' quality of life. Multiple potential treatment options exist for patients (and clinicians) to choose from, with limited evidence to inform treatment selection. The aim was to explore how patients with IBS go about seeking and appraising different treatment modalities, with a view to elucidating the psychological processes involved and identifying opportunities to improve clinical practice. Design: Qualitative study nested within a randomized controlled trial of therapist-delivered and web-based cognitive behavioural therapy versus treatment-as-usual for IBS. Methods: A total of 52 people participated in semi-structured interviews about their prior experiences of treatments for IBS. Transcripts were analysed using inductive thematic analysis. Results: Key themes (desperation for a cure, disappointment at lack of cure, appraising the effects of diverse treatments, and hope for positive effects) clustered around an overarching theme of being trapped within a vicious cycle of hope and despair on treatment seeking. A desperation and willingness drove interviewees to try any treatment modality available that might potentially offer relief. Coming to accept there is no cure for IBS helped interviewees escape the vicious cycle. Treatments were appraised for their effects on symptoms and quality of life while also considering, but rarely prioritizing, other aspects including convenience of the regimen itself, whether it addressed the perceived root causes of IBS, perceived side-effects, and cost. Conclusion: Treatment seeking in IBS can be challenging for patients. Supportive discussions with health care professionals about illness perceptions, treatment beliefs, and goals could improve patients' experiences. Statement of contribution What is already known on this subject? Irritable bowel syndrome (IBS) is a highly prevalent chronic relapsing functional gastrointestinal disorder. Studies show few treatment modalities provide complete symptom relief. IBS is associated with emotional and physical distress, and negatively impacts personal, social, and professional aspects of quality of life. What does this study add? Patients appraise IBS treatments for impact on quality of life and treatment characteristics. Developing acceptance and coping strategies helps escape treatment-seeking vicious cycles of hope and despair. Clinicians could better support patients by discussing their illness perceptions, treatment goals, and values