title: Computer aided detection of epidural masses on computed tomography scans.
abstract: The widespread use of CT imaging and the critical importance of early detection of epidural masses of the spinal canal generate a scenario ideal for the implementation of a computer-aided detection (CAD) system. Epidural masses can lead to paralysis, incontinence and loss of neurological function if not promptly detected. We present, to our knowledge, the first CAD system to detect epidural masses on CT scans. In this paper, spatially constrained Gaussian mixture model (GMM) and supervoxel-based method are proposed for epidural mass detection. The detection is performed on the Gaussian level or the supervoxel level rather than the voxel level. Cross-validation on 40 patients with epidural masses on body CT showed that the supervoxel-based method yielded a significant improvement of performance (82% at 3 false positives per patient) over the spatially constrained GMM method (55% at 3 false positives per patient).
authors.affiliation: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Jiamin
authors.initials: J
authors.lastname: Liu
authors.affiliation: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Sanket
authors.initials: S
authors.lastname: Pattanaik
authors.affiliation: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Yao
authors.affiliation: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Evrim
authors.initials: E
authors.lastname: Turkbey
authors.affiliation: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Weidong
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Xiao
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA. Electronic address: rms@nih.gov.
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Summers
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015174:Epidural Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Computer aided detection; Epidural mass; Gaussian mixture model; Supervoxel