title: Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes.
abstract: The need to segment multiple interacting surfaces is a common problem in medical imaging and it is often assumed that such surfaces are continuous within the confines of the region of interest. However, in some application areas, the surfaces of interest may contain a shared hole in which the surfaces no longer exist and the exact location of the hole boundary is not known a priori. The boundary of the neural canal opening seen in spectral-domain optical coherence tomography volumes is an example of a "hole" embedded with multiple surrounding surfaces. Segmentation approaches that rely on finding the surfaces alone are prone to failures as deeper structures within the hole can "attract" the surfaces and pull them away from their correct location at the hole boundary. With this application area in mind, we present a graph-theoretic approach for segmenting multiple surfaces with a shared hole. The overall cost function that is optimized consists of both the costs of the surfaces outside the hole and the cost of boundary of the hole itself. The constraints utilized were appropriately adapted in order to ensure the smoothness of the hole boundary in addition to ensuring the smoothness of the non-overlapping surfaces. By using this approach, a significant improvement was observed over a more traditional two-pass approach in which the surfaces are segmented first (assuming the presence of no hole) followed by segmenting the neural canal opening.
authors.forename: Bhavna J
authors.initials: BJ
authors.lastname: Antony
authors.forename: Mohammed S
authors.initials: MS
authors.lastname: Miri
authors.forename: Michael D
authors.initials: MD
authors.lastname: Abràmoff
authors.forename: Young H
authors.initials: YH
authors.lastname: Kwon
authors.forename: Mona K
authors.initials: MK
authors.lastname: Garvin
mesh_terms: D000465:Algorithms; D005901:Glaucoma / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D054259:Neural Tube / Q000473:pathology*; D009898:Optic Disk / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D041623:Tomography, Optical Coherence / Q000379:methods*