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Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes
Book chapter   Open access   Peer reviewed

Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes

Bhavna J Antony, Mohammed S Miri, Michael D Abràmoff, Young H Kwon and Mona K Garvin
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part I, pp.739-746
Lecture Notes in Computer Science, v. 8673, Springer
2014
DOI: 10.1007/978-3-319-10404-1_92
PMCID: PMC4372814
PMID: 25333185
url
https://doi.org/10.1007/978-3-319-10404-1_92View
Published (Version of record) Open Access

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.
 Algorithms Reproducibility of Results Humans Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Subtraction Technique Tomography, Optical Coherence - methods Neural Tube - pathology Sensitivity and Specificity Glaucoma - pathology Image Enhancement - methods Optic Disk - pathology Pattern Recognition, Automated - methods

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