Content–based fMRI Brain Maps Retrieval

The statistical analysis of functional magnetic resonance imaging (fMRI) is used to extract functional data of cerebral activation during a given experimental task. It allows for assessing changes in cerebral function related to cerebral activities. This methodology has been widely used and a few initiatives aim to develop shared data resources. Searching these data resources for a specific research goal remains a challenging problem. In particular, work is needed to create a global content–based (CB) fMRI retrieval capability. This work presents a CB fMRI retrieval approach based on the brain activation maps extracted using Probabilistic Independent Component Analysis (PICA). We obtained promising results on data from a variety of experiments which highlight the potential of the system as a tool that provides support for finding hidden similarities between brain activation maps

Graph Representation for Content–based fMRI Activation Map Retrieval

The use of functional magnetic resonance imaging (fMRI) to visualize brain activity in a non–invasive way is an emerging technique in neuroscience. It is expected that data sharing and the development of better search tools for the large amount of existing fMRI data may lead to a better understanding of the brain through the use of larger sample sizes or allowing collaboration among experts in various areas of expertise. In fact, there is a trend toward such sharing of fMRI data, but there is a lack of tools to effectively search fMRI data repositories, a factor which limits further research use of these repositories. Content–based (CB) fMRI brain map retrieval tools may alleviate this problem. A CB–fMRI brain map retrieval tool queries a brain activation map collection (containing brain maps showing activation areas after a stimulus is applied to a subject), and retrieves relevant brain activation maps, i.e. maps that are similar to the query brain activation map. In this work, we propose a graph–based representation for brain activation maps with the goal of improving retrieval accuracy as compared to existing methods. In this brain graph, nodes represent different specialized regions of a functional–based brain atlas. We evaluated our approach using human subject data obtained from eight experiments where a variety of stimuli were applied

Vertebra Shape Classification using MLP for Content-Based Image Retrieval

A desirable content-based image retrieval (CBIR) system would classify extracted image features to support some form of semantic retrieval. The Lister Hill National Center for Biomedical Communications, an intramural R&D division of the National Library for Medicine (NLM), maintains an archive of digitized X-rays of the cervical and lumbar spine taken as part of the second national health and nutrition examination survey (NHANES II). It is our goal to provide shape-based access to digitized X-rays including retrieval on automatically detected and classified pathology, e.g., anterior osteophytes. This is done using radius of curvature analysis along the anterior portion, and morphological analysis for quantifying protrusion regions along the vertebra boundary. Experimental results are presented for the classification of 704 cervical spine vertebrae by evaluating the features using a multi-layer perceptron (MLP) based approach. In this paper, we describe the design and current status of the content-based image retrieval (CBIR) system and the role of neural networks in the design of an effective multimedia information retrieval system

Synthetic Sample Selection via Reinforcement Learning

Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images. Thus, the effectiveness of those synthetic images in medical image recognition systems cannot be guaranteed when they are being added randomly without quality assurance. In this work, we propose a reinforcement learning (RL) based synthetic sample selection method that learns to choose synthetic images containing reliable and informative features. A transformer based controller is trained via proximal policy optimization (PPO) using the validation classification accuracy as the reward. The selected images are mixed with the original training data for improved training of image recognition systems. To validate our method, we take the pathology image recognition as an example and conduct extensive experiments on two histopathology image datasets. In experiments on a cervical dataset and a lymph node dataset, the image classification performance is improved by 8.1% and 2.3%, respectively, when utilizing high-quality synthetic images selected by our RL framework. Our proposed synthetic sample selection method is general and has great potential to boost the performance of various medical image recognition systems given limited annotation.Comment: MICCAI202

Live Wire Segmentation Tool for Osteophyte Detection in Lumbar Spine X-Ray Images

Computer-assisted vertebra segmentation in x-ray images is a challenging problem. Inter-subject variability and the generally poor contrast of digitized radiograph images contribute to the segmentation difficulty. In this paper, a semi-automated live wire approach is investigated for vertebrae segmentation. The live wire approach integrates initially selected user points with dynamic programming to generate a closed vertebra boundary. In order to assess the degree to which vertebra features are conserved using the live wire technique, convex hull-based features to characterize anterior osteophytes in lumbar vertebrae are determined for live wire and manually segmented vertebrae. Anterior osteophyte discrimination was performed over 405 lumbar vertebrae, 204 abnormal vertebrae with anterior osteophytes and 201 normal vertebrae. A leave-one-out standard back propagation neural network was used for vertebrae segmentation. Experimental results show that manual segmentation yielded slightly better discrimination results than the live wire technique

Image Analysis Techniques for the Automated Evaluation of Subaxial Subluxation in Cervical Spine X-Ray Images

Rheumatoid arthritis is a chronic inflammatory disease affecting synovial joints of the body, especially the hands and feet, spine, knees and hips. For many patients, the cervical spine is associated with rheumatoid arthritis. Subluxation is the abnormal movement of one of the bones that comprise a joint. In this research, image analysis techniques have been investigated for the recognition of cervical spine x-ray images with one or more instances of subaxial subluxation. Receiver operating characteristic curve results are presented, showing potential for subaxial subluxation discrimination on an image-by-image basis

Incorporating African American Veterans\u27 Success Stories for Hypertension Management: Developing a Behavioral Support Texting Protocol

BACKGROUND: Peer narratives engage listeners through personally relevant content and have been shown to promote lifestyle change and effective self-management among patients with hypertension. Incorporating key quotations from these stories into follow-up text messages is a novel way to continue the conversation, providing reinforcement of health behaviors in the patients\u27 daily lives. OBJECTIVE: In our previous work, we developed and tested videos in which African American Veterans shared stories of challenges and success strategies related to hypertension self-management. This study aims to describe our process for developing a text-messaging protocol intended for use after viewing videos that incorporate the voices of these Veterans. METHODS: We used a multistep process, transforming video-recorded story excerpts from 5 Veterans into 160-character texts. We then integrated these into comprehensive 6-month texting protocols. We began with an iterative review of story transcripts to identify vernacular features and key self-management concepts emphasized by each storyteller. We worked with 2 Veteran consultants who guided our narrative text message development in substantive ways, as we sought to craft culturally sensitive content for texts. Informed by Veteran input on timing and integration, supplementary educational and 2-way interactive assessment text messages were also developed. RESULTS: Within the Veterans Affairs texting system Annie, we programmed five 6-month text-messaging protocols that included cycles of 3 text message types: narrative messages, nonnarrative educational messages, and 2-way interactive messages assessing self-efficacy and behavior related to hypertension self-management. Each protocol corresponds to a single Veteran storyteller, allowing Veterans to choose the story that most resonates with their own life experiences. CONCLUSIONS: We crafted a culturally sensitive text-messaging protocol using narrative content referenced in Veteran stories to support effective hypertension self-management. Integrating narrative content into a mobile health texting intervention provides a low-cost way to support longitudinal behavior change. A randomized trial is underway to test its impact on the lifestyle changes and blood pressure of African American Veterans. TRIAL REGISTRATION: ClinicalTrials.gov NCT03970590; https://clinicaltrials.gov/ct2/show/NCT03970590. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29423

Named Entity Recognition in Functional Neuroimaging Literature

Human neuroimaging research aims to find mappings between brain activity and broad cognitive states. In particular, Functional Magnetic Resonance Imaging (fMRI) allows collecting information about activity in the brain in a non-invasive way. In this paper, we tackle the task of linking brain activity information from fMRI data with named entities expressed in functional neuroimaging literature. For the automatic extraction of those links, we focus on Named Entity Recognition (NER) and compare different methods to recognize relevant entities from fMRI literature. We selected 15 entity categories to describe cognitive states, anatomical areas, stimuli and responses. To cope with the lack of relevant training data, we proposed rulebased methods relying on noun-phrase detection and filtering. We also developed machine learning methods based on Conditional Random Fields (CRF) with morpho-syntactic and semantic features. We constructed a gold standard corpus to evaluate these different NER methods. A comparison of the obtained F1 scores showed that the proposed approaches significantly outperform three state-of-the-art methods in open and specific domains with a best result of 78.79% F1 score in exact span evaluation and 98.40% F1 in inexact span evaluation

Fine-mapping of the HNF1B multicancer locus identifies candidate variants that mediate endometrial cancer risk.

Common variants in the hepatocyte nuclear factor 1 homeobox B (HNF1B) gene are associated with the risk of Type II diabetes and multiple cancers. Evidence to date indicates that cancer risk may be mediated via genetic or epigenetic effects on HNF1B gene expression. We previously found single-nucleotide polymorphisms (SNPs) at the HNF1B locus to be associated with endometrial cancer, and now report extensive fine-mapping and in silico and laboratory analyses of this locus. Analysis of 1184 genotyped and imputed SNPs in 6608 Caucasian cases and 37 925 controls, and 895 Asian cases and 1968 controls, revealed the best signal of association for SNP rs11263763 (P = 8.4 × 10(-14), odds ratio = 0.86, 95% confidence interval = 0.82-0.89), located within HNF1B intron 1. Haplotype analysis and conditional analyses provide no evidence of further independent endometrial cancer risk variants at this locus. SNP rs11263763 genotype was associated with HNF1B mRNA expression but not with HNF1B methylation in endometrial tumor samples from The Cancer Genome Atlas. Genetic analyses prioritized rs11263763 and four other SNPs in high-to-moderate linkage disequilibrium as the most likely causal SNPs. Three of these SNPs map to the extended HNF1B promoter based on chromatin marks extending from the minimal promoter region. Reporter assays demonstrated that this extended region reduces activity in combination with the minimal HNF1B promoter, and that the minor alleles of rs11263763 or rs8064454 are associated with decreased HNF1B promoter activity. Our findings provide evidence for a single signal associated with endometrial cancer risk at the HNF1B locus, and that risk is likely mediated via altered HNF1B gene expression