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.

A machine-learning graph-based approach for 3D segmentation of Bruch’s membrane opening from glaucomatous SD-OCT volumes

“…Meaning that for each subject in the testing set the average of errors in the 20 slices (with BMO points manually marked on them) are computed and the standard deviation is computed over the averages of the subjects. Based on BMO identification errors, the proposed method outperformed our previous iterative approach (Antony et al, 2014) in the z -direction, r -direction, and r - z plane ( p < 0.05). Similarly, the signed BMO identification error showed that the proposed method has significantly lower errors in the r - and z -directions than the iterative approach ( p < 0.05).…”

“…The performance of the proposed BMO identification method (BMO proposed ) as well as our previous iterative method (Antony et al, 2014) (BMO iterative ) were evaluated using the reference standard obtained on the 20 slices of each subject with manual delineation (BMO manual ). The signed and unsigned distances of automated BMO points with manual BMO points in the r -direction and z- direction, were measured separately.…”

“…Our proposed method in (Antony et al, 2014) was the first step towards directly obtaining a 3D segmentation, where we presented an automated iterative graph-theoretic approach for segmenting multiple surfaces with a shared hole. The method was applied to segment the junction of the inner and outer segments (IS/OS) of the photoreceptors and the Bruch’s membrane (BM) surfaces and their shared hole (i.e.…”

Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

“…Therefore, the retinal layers in ONH centered SD-OCT image have a shared hole. Such discontinuity makes it difficult to accurately segment the layers when the exact position of the hole boundary, i.e., the optic disc boundary are not known a priori [19]. For decades, several studies were devoted to OCT image layer segmentation and analysis.…”

In vivo microvascular network imaging of the human retina combined with an automatic three-dimensional segmentation method

“…A graph cut-based segmentation method was proposed for simultaneous segmentation of multiple 3-D surfaces in the macula 24 and optic nerve head. 25 However, the minimum cut graph algorithm is computationally expensive which usually requires minutes to complete a 3-D data segmentation. Cheng and Lin 26 proposed a fast 3-D dynamic programming expansion method for vessel boundary detection on magnetic resonance imaging sequences.…”