Grant: Discovery Grants Program - Individual
Queen's University
$128,000.00
Information Processing for Medical Image Understanding
EF 2022-23
Résumé
This research program can be phrased as a simple question: ``How can computer science improve a physician's understanding of disease processes?'' In the proposed work, novel displays of complex information will provide tools for exploring metabolic processes and guiding a surgeon to excise all of a cancerous tumor; in orthopedics, improved mathematical modeling will be used to address the highly variable presentation of osteoarthritis in medical images. This proposal is to use the applicant's expertise in computing, and considerable experience in surgery, to give new theoretical results and implementations that will directly improve the health of Canadians.The novel displays will include the auditory presentation of data from mass spectrometry. At Queen's, we have several instruments that can variously capture mass-spectrometry images of tissue, or can capture a data stream as a surgical electric knife slices through tissue. These data are highly multi-spectral and are very difficult to classify automatically. Instead, I propose to transform the mass data to an audible output. This will let a pathologist studying a tissue sample, or a surgeon making real-time decisions in an operating room, to literally hear the sound of cancer. Being able to hear the metabolic products of each cell in a sample would be a world-leading breakthrough in the analysis of tissues for cancer research.The mathematical modeling will include ways to perform computations on high-dimension mathematical manifolds. First, we will develop statistical models of arthritic joints based on the manifolds; next, we will use the statistical models to improve the automatic recognition of arthritic joints in 3D images such as CT or MRI scans. Arthritis is a constellation of diseases that affects nearly every Canadian family, so improved ways of describing it will help the staging of treatments for this debilitating condition.Twenty-four highly qualified personnel will receive multidisciplinary training to prepare them for careers in biomedical computing. Our graduates are highly sought after by medical schools, graduate schools, and information-technology industry sectors.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
RGPIN-2018-04430
Name nom
Ellis, Randy
Department département
Computing, School of - Computing, School of
Organization id
29
Institution établissement
Queen's University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2018
Award amount
128000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
1100
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Health, education and social services
Area of application fr
Santé publique, éducation et services sociaux
Research subject code
2700
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information technology
Research subject fr
Technologie de l'information
Installment
inst:5;6
Partie
2
Nb partie
6
Application title
Information Processing for Medical Image Understanding
Application summary
This research program can be phrased as a simple question: ``How can computer science improve a physician's understanding of disease processes?'' In the proposed work, novel displays of complex information will provide tools for exploring metabolic processes and guiding a surgeon to excise all of a cancerous tumor; in orthopedics, improved mathematical modeling will be used to address the highly variable presentation of osteoarthritis in medical images. This proposal is to use the applicant's expertise in computing, and considerable experience in surgery, to give new theoretical results and implementations that will directly improve the health of Canadians.The novel displays will include the auditory presentation of data from mass spectrometry. At Queen's, we have several instruments that can variously capture mass-spectrometry images of tissue, or can capture a data stream as a surgical electric knife slices through tissue. These data are highly multi-spectral and are very difficult to classify automatically. Instead, I propose to transform the mass data to an audible output. This will let a pathologist studying a tissue sample, or a surgeon making real-time decisions in an operating room, to literally hear the sound of cancer. Being able to hear the metabolic products of each cell in a sample would be a world-leading breakthrough in the analysis of tissues for cancer research.The mathematical modeling will include ways to perform computations on high-dimension mathematical manifolds. First, we will develop statistical models of arthritic joints based on the manifolds; next, we will use the statistical models to improve the automatic recognition of arthritic joints in 3D images such as CT or MRI scans. Arthritis is a constellation of diseases that affects nearly every Canadian family, so improved ways of describing it will help the staging of treatments for this debilitating condition.Twenty-four highly qualified personnel will receive multidisciplinary training to prepare them for careers in biomedical computing. Our graduates are highly sought after by medical schools, graduate schools, and information-technology industry sectors.