Characterizing Phenotypes of Musculoskeletal Degeneration Using Medical Imaging and Deep Learning

Date

June 11, 202106/11/2021 11:00am 06/11/2021 11:00am Characterizing Phenotypes of Musculoskeletal Degeneration Using Medical Imaging and Deep Learning
Join Dr. Sharmila Majumdar as she hosts the exit seminar for Claudia Iriondo. This virtual event will be held on Friday, 11 June at 11AM.
 
Title: Characterizing Phenotypes of Musculoskeletal Degeneration Using Medical Imaging and Deep Learning
 
Abstract:  Musculoskeletal disease is the leading cause of disability worldwide, with the 2019 Global Burden of Disease study reporting global disease prevalence of approximately 1.714 billion. X-ray and magnetic resonance imaging (MRI) are routinely used for clinical diagnosis and monitoring of musculoskeletal disease, however, due to an increasing volume of acquired images and limited time, image assessments are mainly qualitative. This thesis aims to elevate the role of imaging in the assessment of musculoskeletal disease by developing fully automatic image analysis tools to improve analysis sensitivity, speed, and/or precision. We target the two conditions with the highest prevalence and healthcare expenditure in the United States: knee osteoarthritis (OA) and back pain. We use deep learning to develop fully automatic tools for image analysis and demonstrate their utility in the assessment and analysis of research and clinical datasets. I will be presenting four main projects:
(1) A deep learning segmentation method for quantitative analysis of knee cartilage from structural MR imaging to analyze cartilage thickness changes over the span of 8 years
(2) A point cloud algorithm for feature learning from structural and compositional knee MRI to assess the importance of shape and composition features in predicting the onset of OA
(3) A registration pipeline for voxel-based analysis of MR imaging of the lumbar spine to examine local associations between T1ρ, T2, and patient reported outcomes
(4) A curve extraction algorithm for analysis of global spine shape from x-ray imaging to build a shape model that examines 3D spine shape variations in the UCSF patient population
 
 
Date:  Friday, 11 June at 11AM
Link to Join:  zoom link

Meeting ID: 983 8276 2197
Password: MSK (480341) 

2951 America/Los_Angeles public

Type

Seminar

Time Duration

One Hour
Join Dr. Sharmila Majumdar as she hosts the exit seminar for Claudia Iriondo. This virtual event will be held on Friday, 11 June at 11AM.
 
Title: Characterizing Phenotypes of Musculoskeletal Degeneration Using Medical Imaging and Deep Learning
 
Abstract:  Musculoskeletal disease is the leading cause of disability worldwide, with the 2019 Global Burden of Disease study reporting global disease prevalence of approximately 1.714 billion. X-ray and magnetic resonance imaging (MRI) are routinely used for clinical diagnosis and monitoring of musculoskeletal disease, however, due to an increasing volume of acquired images and limited time, image assessments are mainly qualitative. This thesis aims to elevate the role of imaging in the assessment of musculoskeletal disease by developing fully automatic image analysis tools to improve analysis sensitivity, speed, and/or precision. We target the two conditions with the highest prevalence and healthcare expenditure in the United States: knee osteoarthritis (OA) and back pain. We use deep learning to develop fully automatic tools for image analysis and demonstrate their utility in the assessment and analysis of research and clinical datasets. I will be presenting four main projects:
(1) A deep learning segmentation method for quantitative analysis of knee cartilage from structural MR imaging to analyze cartilage thickness changes over the span of 8 years
(2) A point cloud algorithm for feature learning from structural and compositional knee MRI to assess the importance of shape and composition features in predicting the onset of OA
(3) A registration pipeline for voxel-based analysis of MR imaging of the lumbar spine to examine local associations between T1ρ, T2, and patient reported outcomes
(4) A curve extraction algorithm for analysis of global spine shape from x-ray imaging to build a shape model that examines 3D spine shape variations in the UCSF patient population
 
 
Date:  Friday, 11 June at 11AM
Link to Join:  zoom link

Meeting ID: 983 8276 2197
Password: MSK (480341) 

Speakers

Claudia Iriondo
Bioengineering PhD Candidate
UCSF
Dr. Sharmila Majumdar
Professor and Vice Chair for Research
Margaret Hart Surbeck Distinguished Professor
UCSF