Multi-Scale Segmentation and Surface Fitting for Measuring 3-D Macular Holes

Abstract

Macular holes are blinding conditions, where a hole develops in the central part of retina, resulting in reduced central vision. The prognosis and treatment options are related to a number of variables, including the macular hole size and shape. High-resolution spectral domain optical coherence tomography allows precise imaging of the macular hole geometry in three dimensions, but the measurement of these by human observers is time-consuming and prone to high inter- and intra-observer variability, being characteristically measured in 2-D rather than 3-D. We introduce several novel techniques to automatically retrieve accurate 3-D measurements of the macular hole, including: surface area, base area, base diameter, top area, top diameter, height, and minimum diameter. Specifically, we introduce a multi-scale 3-D level set segmentation approach based on a state-of-the-art level set method, and we introduce novel curvature-based cutting and 3-D measurement procedures. The algorithm is fully automatic, and we validate our extracted measurements both qualitatively and quantitatively, where our results show the method to be robust across a variety of scenarios. Our automated processes are considered a significant contribution for clinical applications.

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images: A review

“…Authors Year Preprocessing Segmentation Classification Nasrulloh et al [11] 2018 Yes Yes No Keller et al [26] 2016 Yes Yes No Miri et al [96] 2016 Yes Yes No Zhang et al [5] 2015 Yes Yes No Xu et al [27] 2013 Yes Yes No Liu et al [19] 2011 Yes Yes Yes Duan et al [43] 2017 Yes Yes No Sui et al [28] 2017 [106] 2017 Yes Yes No Athira et al [107] 2018 Yes Yes No Gopinath et al [108] 2017 No Yes No Dodo et al [109] 2019 Yes Yes No Duan et al [110] 2015 Yes Yes No Lang et al [111] 2017 Yes Yes No Niu et al [112] 2014 Yes Yes No Rossant et al [113] 2015 Yes Yes No Tian et al [114] 2015 Yes Yes No Huang et al [80] 2019 No Yes Yes Nath et al [82] 2018 Yes Yes Yes Hassan and Hassan [81] 2019 Yes Yes Yes Hassan et al [1] 2016 Yes Yes Yes Fang et al [115] 2017 Yes Yes Yes the B-scans [93,94]. The OCTID was the only publicly available database found with only cases of macular holes pathology [95].…”

“…The review indicates many types of preprocessing techniques: dilation and erosion [5], median filter [27,107,111], gaussian filter [11,100,101], wiener filter [11,81,99], binary image [26,100], gradient image [26,114], anisotropic difusion filter [5,97,99], image aligment [19,98,103], attenuation coefficient [101], enhanced contrast [1,105], image flattenig [106,114], resize the image [1,107], edge flow [112], sparse filter [112], normalization [81], green channel [81], greyscale [1], morphological operations [1] and others. In Figure 2 is shown the improvements of preprocessing applied to OCT image.…”

“…Some segmentation techniques applied to OCT images are: snakes or active contour, it is a method used to circumvent objects in an image [105]; graph search, this technique starts from a starting point to an ending point, repeating the process, trying to find the best path [5,26,27]; Dijkstra shortest path search, it is an algorithm that seeks to find the shortest path between two nodes [26,99,107]; local gaussian distribution fitting (LGDF), local entropy defines gray level weights [11]; curvature-based surface cutting, it can be flattened into the plane with low metric distortion [11]; ReLayNet, it is an architecture used to segment retinal layers [80,117]; speed-up robust features (SURF), it is a method used in tasks like object recognition, classification etc [115]; adjusted mean arc length (AMAL), it enables to pass the load limit and turning points, and consequently to follow the post-critical equilibrium trajectories [26]; gradient vector flow (GVF), it is an algorithm that locates object edges [96,118,119].…”

“…This section analysis the best works exploring the most recent and important approaches for automated segmentation in OCT images. Nasrulloh [11] developed a technique to extract measurements from the automated segmentation of MH. In this technique, after the preprocessing step, the segmentation is processed by LGDF and Curvature-based Surface Cutting.…”

“…The main effects due this pathology is the vision reduction or even total blindness [11]. It can cause negative impacts on the quality of life because the limitations of vision.…”