October 9 - 12, 2012
Building 10 & Natcher Conference Center
- General Schedule of Events
- Opening Plenary Session
- Concurrent Symposia Sessions
- Poster Sessions
- National Graduate Student Research Conference
- FARE Award Ceremony
- Special Exhibits on Resources for Intramural Research
- TSA Research Festival Exhibit Show
- Clinical Center Tours
- Research Festival Committees
- Past Research Festivals
Computer-Aided Detection of Epidural Masses in Computed Tomography using a Constrained Gaussian Mixture Model Framework
| Thursday, November 07, 2013 — Poster Session II | |||
|---|---|---|---|
12:00 p.m. – 2:00 p.m. |
FAES Academic Center (Upper-Level Terrace) |
CC |
BIOENG-20 |
Authors
- s Pattanaik
- j Liu
- J Yao
- W Zhang
- EB Turkbey
- RM Summers
Abstract
PURPOSE: Our preliminary Computer-Aided Detection (CAD) framework aims to localize candidate epidural mass detections and reduce false positives. MATERIALS AND METHODS: 23 patients with chest-abdomen-pelvis CT with confirmed epidural masses were selected. 17 patients without mass were served as controls. Two radiologists manually demarcated the ground truth. The CAD system segments whole spine using a watershed algorithm and directed graph search. It also isolates the spinal canal. Four tissue classes were generated using K-means clustering to represent normal intradural tissue, fat/vasculature, the epidural mass, and a partial volume region between the bone and soft tissue. CGMM was employed to refine classification, taking advantage of both spatial and intensity parameters. Detections were limited to masses extending from the canal boundary. RESULTS: Before classification with SVM, our CAD system detected 44 out of 47 detections. A sensitivity of 80% with 7.2 false positives per patient was attained following classification and ten-fold cross-validation, which compared favorably against the sensitivity of 76% with 7.4 false positives per patient attained by restricting CAD to intensity based K-Means clustering. CONCLUSION: Our CAD system lays the groundwork for detection of epidural masses in CT using a combination of spatial and intensity based parameters to localize epidural masses.
