Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning

The choroid layer is a vascular layer in human retina and its main function is to provide oxygen and support to the retina. Various studies have shown that the thickness of the choroid layer is correlated with the diagnosis of several ophthalmic diseases. For example, diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. Despite contemporary advances, automatic segmentation of the choroid layer remains a challenging task due to low contrast, inhomogeneous intensity, inconsistent texture and ambiguous boundaries between the choroid and sclera in Optical Coherence Tomography (OCT) images. The majority of currently implemented methods manually or semi-automatically segment out the region of interest. While many fully automatic methods exist in the context of choroid layer segmentation, more effective and accurate automatic methods are required in order to employ these methods in the clinical sector. This paper proposed and implemented an automatic method for choroid layer segmentation in OCT images using deep learning and a series of morphological operations. The aim of this research was to segment out Bruch’s Membrane (BM) and choroid layer to calculate the thickness map. BM was segmented using a series of morphological operations, whereas the choroid layer was segmented using a deep learning approach as more image statistics were required to segment accurately. Several evaluation metrics were used to test and compare the proposed method against other existing methodologies. Experimental results showed that the proposed method greatly reduced the error rate when compared with the other state-of-the art methods

Choroidal OCT Analytics

The advance in OCT imaging techniques allow visualization of the deeper structures of the eye, including the choroid, the structural change of which is associated with various diseases. In this chapter, methodologies are presented for automatic quantification of choroidal measurements such as thickness, volume and stromal-luminal ratio, which are indicators crucial in disease diagnosis and treatment response monitoring

Correlation of structure and function of the macula in patients with retinitis pigmentosa

PurposeTo correlate the structure of the macula, as measured by spectral-domain optical coherence tomography (SD-OCT) and function, as measured by microperimetry (MAIA) in patients with retinitis pigmentosa (RP) and relatively good visual acuity.DesignProspective, cross-sectional, non-intervention study.SubjectsPatients with RP.MethodsThirty patients with RP and good central visual acuity were identified. Each patient underwent SD-OCT of the macula and microperimetry. The images were overlaid using the custom-designed software. The retinal sensitivity by microperimetry was correlated with corresponding retinal thickness, as measured by the SD-OCT. ELM, COST, and IS/OS junction were scored as intact, disrupted, or absent.Main outcome measuresComparing the retinal sensitivity on the MAIA with various measurements on the SD-OCT.ResultsThe retinal sensitivity on the MAIA showed a significant correlation with total retinal thickness and outer retinal thickness on the SD-OCT. There was no association with either the inner retinal thickness or the choroidal thickness. ORT showed a statistically significant correlation with the anatomical classification of ELM (r=-0.76, P<0.001), IS/OS (r=-0.800, P<0.001), and COST (r=-0.733, P<0.001).ConclusionThis study determined that there was a high correlation of the structure and function of the central macula in patients with RP. These studies are important to establish surrogate markers that can be used as end points for various tests in future therapeutic clinical trials.Eye advance online publication, 8 May 2015; doi:10.1038/eye.2015.61