A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB (denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm 0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from $0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort

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

An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography

Purpose: To develop an open-source, fully-automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography (OCT) data. Methods: We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes) from 3 clinical studies related to systemic disease. Ground truth segmentations were generated using a clinically validated, semi-automatic choroid segmentation method, Gaussian Process Edge Tracing (GPET). We finetuned a UNet with MobileNetV3 backbone pre-trained on ImageNet. Standard segmentation agreement metrics, as well as derived measures of choroidal thickness and area, were used to evaluate DeepGPET, alongside qualitative evaluation from a clinical ophthalmologist. Results: DeepGPET achieves excellent agreement with GPET on data from 3 clinical studies (AUC=0.9994, Dice=0.9664; Pearson correlation of 0.8908 for choroidal thickness and 0.9082 for choroidal area), while reducing the mean processing time per image on a standard laptop CPU from 34.49s ($\pm$15.09) using GPET to 1.25s ($\pm$0.10) using DeepGPET. Both methods performed similarly according to a clinical ophthalmologist, who qualitatively judged a subset of segmentations by GPET and DeepGPET, based on smoothness and accuracy of segmentations. Conclusions :DeepGPET, a fully-automatic, open-source algorithm for choroidal segmentation, will enable researchers to efficiently extract choroidal measurements, even for large datasets. As no manual interventions are required, DeepGPET is less subjective than semi-automatic methods and could be deployed in clinical practice without necessitating a trained operator. DeepGPET addresses the lack of open-source, fully-automatic and clinically relevant choroid segmentation algorithms, and its subsequent public release will facilitate future choroidal research both in ophthalmology and wider systemic health.Comment: 8 pages, 2 figures, 3 tables. Currently in submission to ARVO TVST (Association for Research in Vision and Ophthalmology, Translational Vision Science & Technology). GitHub link to codebase provided upon publicatio

Computational Methods for Image Acquisition and Analysis with Applications in Optical Coherence Tomography

The computational approach to image acquisition and analysis plays an important role in medical imaging and optical coherence tomography (OCT). This thesis is dedicated to the development and evaluation of algorithmic solutions for better image acquisition and analysis with a focus on OCT retinal imaging. For image acquisition, we first developed, implemented, and systematically evaluated a compressive sensing approach for image/signal acquisition for single-pixel camera architectures and an OCT system. Our evaluation outcome provides a detailed insight into implementing compressive data acquisition of those imaging systems. We further proposed a convolutional neural network model, LSHR-Net, as the first deep-learning imaging solution for the single-pixel camera. This method can achieve better accuracy, hardware-efficient image acquisition and reconstruction than the conventional compressive sensing algorithm. Three image analysis methods were proposed to achieve retinal OCT image analysis with high accuracy and robustness. We first proposed a framework for healthy retinal layer segmentation. Our framework consists of several image processing algorithms specifically aimed at segmenting a total of 12 thin retinal cell layers, outperforming other segmentation methods. Furthermore, we proposed two deep-learning-based models to segment retinal oedema lesions in OCT images, with particular attention on processing small-scale datasets. The first model leverages transfer learning to implement oedema segmentation and achieves better accuracy than comparable methods. Based on the meta-learning concept, a second model was designed to be a solution for general medical image segmentation. The results of this work indicate that our model can be applied to retinal OCT images and other small-scale medical image data, such as skin cancer, demonstrated in this thesis