Cornell University

Current Research Project Listings:

The Computer Vision and Image Analysis group at Cornell University is involved in various research projects with the primary goal of the development of medical computer aided diagnosis systems. Other ongoing projects also are involved in the creation of programs and systems that can be used in a research setting. The following is a listing of all ongoing graduate research projects. All projects in VIA group are under the supervision of A.P. Reeves: Computer Vision and Medical Image Analysis Overview

 

VIA Research Projects:

	Automated Chest Health Analysis Automated determination of the anatomy in a CT scan has two extremely useful outcomes: on the one hand it can be used to screen for pathologies, especially rare ones; on the other hand it can serve as the starting point for further investigation by providing landmarks for refined segmentations.
	3D Optical Cell-CT Analysis The 3D scans of single cells are processed and analysed to provide new tools for their evaluation.
	Automated Coronary Calcium Scoring Estimation of calcium burden in coronaries is a well know risk factor. The main goal of this research is to perform a fully automated estimation in un-gated, low-dose CT scans.
	Human Airway Analysis: Jaesung Lee Many pulmonary diseases affect the dimensions of airways. By measuring the airway dimensions to a known precision, the clinicians will be able to better diagnose and monitor the patients.
	Vessel Measurement in Intravital Microscopy: Jaesung Lee Measuring the vessel diameters over time is a crucial step in studying the effect of various stimuli on the width of blood vessel nearby. Automatic measurement provides for fast and accurate tracking of vessel diameter.
	Pulmonary nodule analysis on CT scans: Artit Jirapatnakul Lung cancer screening is poised to become a major practice in the near future. As long as patients are undergoing radiation for early lung cancer detection, it is only logical to analyze the same images to diagnose other diseases. Currently, computer algorithms have been developed to measure solid pulmonary nodules, compute growth rates, and determine whether a nodule is malignant or benign from a single scan.
	Automated Emphysema Quantification CT scan data allows for the measurement of the anatomical basis of emphysema and is being used to determine disease state and progression in clinical cases. Many metrics have been proposed to allow for automated quantification. This research is primarily focused on the analysis of variation of the most commonly used measures to better reflect true progression of disease versus the natural variation present in these measures.
	Catherter Tip Location Identification: Brad Keller Devolpment of a CAD system to allow for more rapid identification of catheter tips and their location in standard PA chest radiographs. This has applications in intesive care unit reading settings where there is a need to increase throughput due to the both volume of scans and the resultant time-consuming nature of the reads.
	Coronary arteries in CTA: Sergei Fotin Decision making in heart disease diagnosis relies heavily on the automated analysis of cardiac images. Here we develop a method for computerized extraction of coronary arteries from CT angiography data.
	Automated nodule detection: Sergei Fotin Examining three-dimensional lung CT images for tiny early-stage nodules is a very tedious and time consuming task for the radiologist. The main goal of this research work is to design and validate an automated system that will detect all cancers without raising too many false alarms.
	Metrics for lesion size estimation: Metrics Group Accurate and reliable measurement of pulmonary nodule size from CT scans has an important role in computer assisted evaluation of lung lesions. This research project aims at studying the reciprocal relationships between different size estimation methods, investingating both the differences in the actual sizes reported and the effects on readers' variability.
	3D Point Mapping from Mouse Skull Micro-CT: Jaesung Lee Micro-CT scans allow for the researchers to study phenotypes of small mice. A convenient tool is developed for mapping a 3D point on the mouse skull surface from micro-CT. The mapped points may be used for calculating various dimensions of the skull.
	Murine lung tumor measurement on Micro-CT scans: Artit Jirapatnakul Mouse models are valuable for studying thedevelopment of human cancer and genetic diseases. Micro-CT scans enableresearchers to monitor the progression of disease starting from a much earlierstage than possible by observing symptoms. Algorithms are being developed toaccurately and robustly measure lung tumors in from micro-CT scans.
GGO Characterization: Andrew Browder, Artit Jirapatnakul Improvements in CT technology and increasing number of accumulated cases have allowed radiologists to find a new type of abnormality, referred to in the literature as GGO or subsolid nodule. These abnormalities have radiological appearances and malignancy rates that are different from those of solid nodules. Characterization of the subsolid nodules could lead to better knowledge about their evolution and better screening protocols
Dysplastic Hips in Post-Natal Femur: Wendy Vandenberg-Foels Osteoarthritis is the leading cause of adult disability in the United States. Approximately 40% of idiopathic osteoarthritis (OA) cases can be attributed to developmental dysplasia of the hip (DDH). While the endstage OA has been the subject of much research, little is known, about the early postnatal development of the dysplastic hip. The objective of this research is to quantitatively describe the temporal development of normal and dysplastic hips