A5079851497- Functional Brain Connectivity Studies
- Advanced Neuroimaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Neonatal and fetal brain pathology
- Brain Tumor Detection and Classification
- Brain Metastases and Treatment
- Medical Imaging Techniques and Applications
- Glioma Diagnosis and Treatment
- Prenatal Substance Exposure Effects
- Alzheimer's disease research and treatments
- Medical Imaging and Analysis
- Advanced X-ray and CT Imaging
- Advanced MRI Techniques and Applications
- EEG and Brain-Computer Interfaces
- Epilepsy research and treatment
- Clusterin in disease pathology
- Vascular Malformations and Hemangiomas
- Soft tissue tumor case studies
- Dementia and Cognitive Impairment Research
- Fetal and Pediatric Neurological Disorders
- Birth, Development, and Health
- Mitochondrial Function and Pathology
- Attention Deficit Hyperactivity Disorder
- Machine Learning in Bioinformatics
- Medical Image Segmentation Techniques
University of California, San Francisco
2017-2024
Hospital of the University of Pennsylvania
2021-2022
University of California, Los Angeles
2010-2013
Children's Hospital of Los Angeles
2011-2012
Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and reports from the parents teachers. Considering its high prevalence large economic societal costs, a quantitative tool that aids diagnosis characterizing underlying neurobiology would be extremely valuable. This provided motivation for ADHD-200 machine learning (ML) competition, multisite collaborative effort to investigate imaging...
Radiosurgery(UCSF-BMSR) dataset is a public, clinical, multimodal brain MRI consisting of 560 MRIs from 412 patients with expert annotations 5136 metastases.Data consists registered and skull stripped T1 post-contrast, pre-contrast, FLAIR subtraction (T1 precontrast -T1 post-contrast) images voxelwise segmentations enhancing metastases in NifTI format.The also includes patient demographics, surgical status primary cancer types.The UCSF-BSMR has been made publicly available the hopes that...
Exercise has been shown to have positive effects on the brain and behavior throughout various stages of lifespan. However, little is known about impact exercise neurodevelopment during adolescent years, particularly with regard white matter microstructure, as assessed by diffusion tensor imaging (DTI). Both tract-based spatial statistics (TBSS) tractography-based along-tract were utilized examine relationship between microstructure aerobic in males, ages 15-18. Furthermore, we examined data...
To develop and validate a neural network for automated detection segmentation of intracranial metastases on brain MRI studies obtained stereotactic radiosurgery treatment planning.
The C allele at the rs11136000 locus in clusterin (CLU) gene is third strongest known genetic risk factor for late-onset Alzheimer's disease (LOAD). A recent genome-wide association study of LOAD found evidence with CLU rs1532278, high linkage disequilibrium rs11136000. Brain structure and function are related to alleles, not just patients but also healthy young adults. We tracked volume lateral ventricles across baseline, 1-year, 2-year follow-up scans a large sample elderly human...
An important challenge in segmenting real-world biomedical imaging data is the presence of multiple disease processes within individual subjects. Most adults above age 60 exhibit a variable degree small vessel ischemic disease, as well chronic infarcts, which will manifest white matter hyperintensities (WMH) on brain MRIs. Subjects diagnosed with gliomas also typically some abnormal T2 signal due to WMH, rather than just tumor. We sought develop fully-automated algorithm distinguish and...
Purpose To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed another institution, and to the effect of multi-institutional training datasets for mitigating performance loss. Materials Methods In this retrospective study, three-dimensional U-Net abnormality was on data from 293 patients (IN1) (median age, 54 years; 165 women; treated between 2008 2018) tested 51 second (IN2) 46 27 2003 2019). The model then additional various sources: (a) 285...
Neural networks were trained for segmentation and longitudinal assessment of posttreatment diffuse glioma. A retrospective cohort (from January 2018 to December 2019) 298 patients with glioma (mean age, 52 years ± 14 [SD]; 177 men; 152 glioblastoma, 72 astrocytoma, 74 oligodendroglioma) who underwent two consecutive multimodal MRI examinations randomly selected into training (n = 198) testing 100) samples. tumor three-dimensional nnU-Net convolutional neural network multichannel inputs (T1,...
The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) dataset is a public, clinical, multimodal brain MRI consisting 560 MRIs from 412 patients with expert annotations 5136 metastases. Data consists registered and skull stripped T1 post-contrast, pre-contrast, FLAIR subtraction (T1 pre-contrast - post-contrast) images voxelwise segmentations enhancing metastases in NifTI format. also includes patient demographics, surgical status primary cancer...