title: Co-segmentation of functional and anatomical images.
abstract: This paper presents a novel method for segmenting functional and anatomical structures simultaneously. The proposed method unifies domains of anatomical and functional images (PET-CT), represents them in a product lattice, and performs simultaneous delineation of regions based on a random walk image segmentation. In addition, we propose a simple yet efficient object/background seed localization method, where background and foreground object cues are automatically obtained from PET images and propagated onto the corresponding anatomical images (CT). In our experiments, abnormal anatomies on PET-CT images from human subjects are segmented synergistically by the proposed fully automatic co-segmentation method with high precision (mean DSC of 91.44%) in seconds (avg. 40 seconds).
authors.affiliation: Center for Infectious Diseases Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, USA.
authors.forename: Ulas
authors.initials: U
authors.lastname: Bagci
authors.forename: Jayaram K
authors.initials: JK
authors.lastname: Udupa
authors.forename: Jianhua
authors.initials: J
authors.lastname: Yao
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Mollura
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008171:Lung Diseases / Q000175:diagnosis*; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography; D015203:Reproducibility of Results; D012129:Respiratory Function Tests / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed
keywords: Joint Segmentation; Object Detection; PET-CT; Random Walk