Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ Segmentation

U-Net has been widely used for segmenting abdominal organs, achieving promising performance. However, when it is used for multi-organ segmentation, first, it may be limited in exploiting global long-range contextual information due to the implementation of standard convolutions. Second, the use of spatial-wise downsampling (e.g., max pooling or strided convolutions) in the encoding path may lead to the loss of deformable or discriminative details. Third, features upsampled from the higher level are concatenated with those that persevered via skip connections. However, repeated downsampling and upsampling operations lead to misalignments between them and their concatenation degrades segmentation performance. To address these limitations, we propose Dynamically Calibrated Convolution (DCC), Dynamically Calibrated Downsampling (DCD), and Dynamically Calibrated Upsampling (DCU) modules, respectively. The DCC module can utilize global inter-dependencies between spatial and channel features to calibrate these features adaptively. The DCD module enables networks to adaptively preserve deformable or discriminative features during downsampling. The DCU module can dynamically align and calibrate upsampled features to eliminate misalignments before concatenations. We integrated the proposed modules into a standard U-Net, resulting in a new architecture, termed Dynamic U-Net. This architectural design enables U-Net to dynamically adjust features for different organs. We evaluated Dynamic U-Net in two abdominal multi-organ segmentation benchmarks. Dynamic U-Net achieved statistically improved segmentation accuracy compared with standard U-Net. Our code is available at https://github.com/sotiraslab/DynamicUNet.Comment: 11 pages, 3 figures, 2 table

Developing methods to detect and diagnose chronic traumatic encephalopathy during life: Rationale, design, and methodology for the DIAGNOSE CTE Research Project

BACKGROUND: Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease that has been neuropathologically diagnosed in brain donors exposed to repetitive head impacts, including boxers and American football, soccer, ice hockey, and rugby players. CTE cannot yet be diagnosed during life. In December 2015, the National Institute of Neurological Disorders and Stroke awarded a seven-year grant (U01NS093334) to fund the Diagnostics, Imaging, and Genetics Network for the Objective Study and Evaluation of Chronic Traumatic Encephalopathy (DIAGNOSE CTE) Research Project. The objectives of this multicenter project are to: develop in vivo fluid and neuroimaging biomarkers for CTE; characterize its clinical presentation; refine and validate clinical research diagnostic criteria (i.e., traumatic encephalopathy syndrome [TES]); examine repetitive head impact exposure, genetic, and other risk factors; and provide shared resources of anonymized data and biological samples to the research community. In this paper, we provide a detailed overview of the rationale, design, and methods for the DIAGNOSE CTE Research Project. METHODS: The targeted sample and sample size was 240 male participants, ages 45-74, including 120 former professional football players, 60 former collegiate football players, and 60 asymptomatic participants without a history of head trauma or participation in organized contact sports. Participants were evaluated at one of four U.S. sites and underwent the following baseline procedures: neurological and neuropsychological examinations; tau and amyloid positron emission tomography; magnetic resonance imaging and spectroscopy; lumbar puncture; blood and saliva collection; and standardized self-report measures of neuropsychiatric, cognitive, and daily functioning. Study partners completed similar informant-report measures. Follow-up evaluations were intended to be in-person and at 3 years post-baseline. Multidisciplinary diagnostic consensus conferences are held, and the reliability and validity of TES diagnostic criteria are examined. RESULTS: Participant enrollment and all baseline evaluations were completed in February 2020. Three-year follow-up evaluations began in October 2019. However, in-person evaluation ceased with the COVID-19 pandemic, and resumed as remote, 4-year follow-up evaluations (including telephone-, online-, and videoconference-based cognitive, neuropsychiatric, and neurologic examinations, as well as in-home blood draw) in February 2021. CONCLUSIONS: Findings from the DIAGNOSE CTE Research Project should facilitate detection and diagnosis of CTE during life, and thereby accelerate research on risk factors, mechanisms, epidemiology, treatment, and prevention of CTE. TRIAL REGISTRATION: NCT02798185

The Open-Source Neuroimaging Research Enterprise

While brain imaging in the clinical setting is largely a practice of looking at images, research neuroimaging is a quantitative and integrative enterprise. Images are run through complex batteries of processing and analysis routines to generate numeric measures of brain characteristics. Other measures potentially related to brain function – demographics, genetics, behavioral tests, neuropsychological tests – are key components of most research studies. The canonical scanner – PACS – viewing station axis used in clinical practice is therefore inadequate for supporting neuroimaging research. Here, we model the neuroimaging research enterprise as a workflow. The principal components of the workflow include data acquisition, data archiving, data processing and analysis, and data utilization. We also describe a set of open-source applications to support each step of the workflow and the transitions between these steps. These applications include DIGITAL IMAGING AND COMMUNICATIONS IN MEDICINE viewing and storage tools, the EXTENSIBLE NEUROIMAGING ARCHIVE TOOLKIT data archiving and exploration platform, and an engine for running processing/analysis pipelines. The overall picture presented is aimed to motivate open-source developers to identify key integration and communication points for interoperating with complimentary applications

Machine learning analytics of resting-state functional connectivity predicts survival outcomes of glioblastoma multiforme patients

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62-0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients

Co-Clinical Imaging Resource Program (CIRP): Bridging the translational divide to advance precision medicine

The National Institutes of Health\u27s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine

MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network

Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. For this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade II-IV) were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41), The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD; n = 774). A 2.5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status by leveraging imaging features from MR scans and prior knowledge features from clinical records and tumor location. The models were tested on one internal (TCGA) and two external (WUSM and EGD) test sets. For IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively. The high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform a 'virtual biopsy' for tailoring treatment planning and overall clinical management of gliomas

The Effects of Vitamin D Deficiency on Neurodegenerative Diseases

Approximately 90% of the elderly population in the western countries has at least a mild to moderate vitamin D hypovitaminosis. Besides the well-known function of vitamin D in calcium homeostasis, it has been recently found that several enzymes and receptors involved in its homeostasis are expressed in the nervous system and brain suggesting also an important role in the brain homeostasis. Interestingly, epidemiological and clinical studies found reduced vitamin D level associated with an increased risk of several neurodegenerative disorders. In this chapter, we focus on a potential link between vitamin D and Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, prion disease, and motor neuron disease. Epidemiological studies were summarized, an overview of the known potential underlying pathomolecular mechanisms are given, and results from clinical studies dealing with vitamin D supplementation were presented. As an outlook, recent literature suggesting an impact of vitamin D on autism spectrum disease, depression, and schizophrenia are briefly discussed. In conclusion, the identification of an abundant vitamin D metabolism in the brain and the tight link between the increasing number of several neurological and mental disorders emphasize the need of further research making a clear recommendation of the intake and supplementation of vitamin D in a growing elderly population

Ceramide remodeling and risk of cardiovascular events and mortality

BackgroundRecent studies suggest that circulating concentrations of specific ceramide species may be associated with coronary risk and mortality. We sought to determine the relations between the most abundant plasma ceramide species of differing acyl chain lengths and the risk of coronary heart disease (CHD) and mortality in community‐based samples. Methods and ResultsWe developed a liquid chromatography/mass spectrometry assay to quantify plasma C24:0, C22:0, and C16:0 ceramides and ratios of these very–long‐chain/long‐chain ceramides in 2642 FHS (Framingham Heart Study) participants and in 3134 SHIP (Study of Health in Pomerania) participants. Over a mean follow‐up of 6 years in FHS, there were 88 CHD and 90 heart failure (HF) events and 239 deaths. Over a median follow‐up time in SHIP of 5.75 years for CHD and HF and 8.24 years for mortality, there were 209 CHD and 146 HF events and 377 deaths. In meta‐analysis of the 2 cohorts and adjusting for standard CHD risk factors, C24:0/C16:0 ceramide ratios were inversely associated with incident CHD (hazard ratio per average SD increment, 0.79; 95% confidence interval, 0.71–0.89; P<0.0001) and inversely associated with incident HF (hazard ratio, 0.78; 95% confidence interval, 0.61–1.00; P=0.046). Moreover, the C24:0/C16:0 and C22:0/C16:0 ceramide ratios were inversely associated with all‐cause mortality (C24:0/C16:0: hazard ratio, 0.60; 95% confidence interval, 0.56–0.65; P<0.0001; C22:0/C16:0: hazard ratio, 0.65; 95% confidence interval, 0.60–0.70; P<0.0001). ConclusionsThe ratio of C24:0/C16:0 ceramides in blood may be a valuable new biomarker of CHD risk, HF risk, and all‐cause mortality in the community

Gene-SGAN: Discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer\u27s disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes