title: A fully automated tortuosity quantification system with application to corneal nerve fibres in confocal microscopy images.
abstract: Recent clinical research has highlighted important links between a number of diseases and the tortuosity of curvilinear anatomical structures like corneal nerve fibres, suggesting that tortuosity changes might detect early stages of specific conditions. Currently, clinical studies are mainly based on subjective, visual assessment, with limited repeatability and inter-observer agreement. To address these problems, we propose a fully automated framework for image-level tortuosity estimation, consisting of a hybrid segmentation method and a highly adaptable, definition-free tortuosity estimation algorithm. The former combines an appearance model, based on a Scale and Curvature-Invariant Ridge Detector (SCIRD), with a context model, including multi-range learned context filters. The latter is based on a novel tortuosity estimation paradigm in which discriminative, multi-scale features can be automatically learned for specific anatomical objects and diseases. Experimental results on 140 in vivo confocal microscopy images of corneal nerve fibres from healthy and unhealthy subjects demonstrate the excellent performance of our method compared to state-of-the-art approaches and ground truth annotations from 3 expert observers.
authors.affiliation: Computer Vision and Image Processing Group, VAMPIRE Project, School of Science and Engineering (Computing), University of Dundee, Dundee, UK. Electronic address: r.annunziata@dundee.ac.uk.
authors.forename: Roberto
authors.initials: R
authors.lastname: Annunziata
authors.affiliation: Ocular Surface Imaging Center, Cornea Service, Massachusetts Eye & Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, USA.
authors.forename: Ahmad
authors.initials: A
authors.lastname: Kheirkhah
authors.affiliation: Ocular Surface Imaging Center, Cornea Service, Massachusetts Eye & Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, USA.
authors.forename: Shruti
authors.initials: S
authors.lastname: Aggarwal
authors.affiliation: Boston Image Reading Center and Cornea Service, New England Eye Center, Tufts Medical Center, Boston, USA; Ocular Surface Imaging Center, Cornea Service, Massachusetts Eye & Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, USA.
authors.forename: Pedram
authors.initials: P
authors.lastname: Hamrah
authors.affiliation: Computer Vision and Image Processing Group, VAMPIRE Project, School of Science and Engineering (Computing), University of Dundee, Dundee, UK.
authors.forename: Emanuele
authors.initials: E
authors.lastname: Trucco
mesh_terms: D000465:Algorithms; D003315:Cornea / Q000000981:diagnostic imaging* / Q000294:innervation*; D064368:Healthy Volunteers; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D009412:Nerve Fibers; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: Automated; Cornea; Curvature; Multiscale; Segmentation; Tortuosity