A three-dimensional optical coherence tomography imaging method based on Lissajous scanning is presented. This method was designed to correct eye motion in OCT images. A Lissajous scanning pattern, which has a trajectory that frequently overlaps with itself, is adopted as the OCT scanning protocol to obtain measurement data. Eye motion artifacts are then corrected automatically by software. By comparing the images without and with motion correction, we show the effectiveness of our method. We performed an experiment and compared the results obtained by our method with the ground truths to verify its validity. The experimental results showed that our method effectively corrects eye motion artifacts. Furthermore, the sufficient repeatability of our method was confirmed.

Lesion detection is a critical component of disease diagnosis, but the manual segmentation of lesions in medical images is time-consuming and experience-demanding. These issues have recently been addressed through deep learning models. However, most of the existing algorithms were developed using supervised training, which requires time-intensive manual labeling and prevents the model from detecting unaware lesions. As such, this study proposes a weakly supervised learning network based on CycleGAN for lesions segmentation in full-width optical coherence tomography (OCT) images. The model was trained to reconstruct underlying normal anatomic structures from abnormal input images, then the lesions can be detected by calculating the difference between the input and output images. A customized network architecture and a multi-scale similarity perceptual reconstruction loss were used to extend the CycleGAN model to transfer between objects exhibiting shape deformations. The proposed technique was validated using an open-source retinal OCT image dataset. Image-level anomaly detection and pixel-level lesion detection results were assessed using area-under-curve (AUC) and the Dice similarity coefficient, producing results of 96.94% and 0.8239, respectively, higher than all comparative methods. The average test time required to generate a single full-width image was 0.039 s, which is shorter than that reported in recent studies. These results indicate that our model can accurately detect and segment retinopathy lesions in real-time, without the need for supervised labeling. And we hope this method will be helpful to accelerate the clinical diagnosis process and reduce the misdiagnosis rate.

To correct eye motion artifacts in en face optical coherence tomography angiography (OCT-A) images, a Lissajous scanning method with subsequent software-based motion correction is proposed. The standard Lissajous scanning pattern is modified to be compatible with OCT-A and a corresponding motion correction algorithm is designed. The effectiveness of our method was demonstrated by comparing en face OCT-A images with and without motion correction. The method was further validated by comparing motion-corrected images with scanning laser ophthalmoscopy images, and the repeatability of the method was evaluated using a checkerboard image. A motion-corrected en face OCT-A image from a blinking case is presented to demonstrate the ability of the method to deal with eye blinking. Results show that the method can produce accurate motion-free en face OCT-A images of the posterior segment of the eye in vivo.

Background: Fundus fluorescein angiography (FA) can be used to diagnose fundus diseases by observing dynamic fluorescein changes that reflect vascular circulation in the fundus. As FA may pose a risk to patients, generative adversarial networks have been used to convert retinal fundus images into fluorescein angiography images. However, the available methods focus on generating FA images of a single phase, and the resolution of the generated FA images is low, being unsuitable for accurately diagnosing fundus diseases. Methods: We propose a network that generates multi-frame high-resolution FA images. This network consists of a low-resolution GAN (LrGAN) and a high-resolution GAN (HrGAN), where LrGAN generates low-resolution and full-size FA images with global intensity information, HrGAN takes the FA images generated by LrGAN as input to generate multi-frame high-resolution FA patches. Finally, the FA patches are merged into full-size FA images. Results: Our approach combines supervised and unsupervised learning methods and achieves better quantitative and qualitative results than using either method alone. Structural similarity (SSIM), normalized cross-correlation (NCC) and peak signal-to-noise ratio (PSNR) were used as quantitative metrics to evaluate the performance of the proposed method. The experimental results show that our method achieves better quantitative results with structural similarity of 0.7126, normalized cross-correlation of 0.6799, and peak signal-to-noise ratio of 15.77. In addition, ablation experiments also demonstrate that using a shared encoder and residual channel attention module in HrGAN is helpful for the generation of high-resolution images. Conclusions: Overall, our method has higher performance for generating retinal vessel details and leaky structures in multiple critical phases, showing a promising clinical diagnostic value.

Geometrical analysis of cone photoreceptor cells is important not only for ophthalmic diagnosis, but also for research on eye diseases. In this study, an automated cone photoreceptor cell segmentation and identification method based on morphological processing and watershed algorithm is presented for adaptive optics scanning laser ophthalmoscope images. Our method includes steps for image denoising, rough segmentation, fine segmentation, small region removal, and identification. The effectiveness of the proposed method was confirmed by comparing its results with those obtained manually yielding precision, recall, and F1-score values of 93.6%, 98.0% and 95.8%, respectively. The performance of our method is further verified by processing images with different cone photoreceptor cell densities from healthy retina and an image from an eye with diabetic retinopathy. The experimental results show that our algorithm achieved high accuracy in cone photoreceptor cell segmentation and identification in healthy retinas as well as in retina with diabetic retinopathy.

A three-dimensional optical coherence tomography (OCT) crystalline lens imaging method based on oblique scanning and image stitching is presented. The method was designed to increase OCT imaging volume of crystalline lens in vivo. A long-range swept-source (SS)-OCT imaging system, which can measure the entire anterior segment of eye in a single acquisition, is used to acquire one central volume and 4 extra volumes with different angles between optical axis of OCT instrument and the pupillary axis. The volumes are then stitched automatically by developed software. To show its effectiveness and verify its validity, we scanned the subjects before and after pupil dilation drops and compared the experimental results. By determining the number of voxels representing the signal from the crystalline lens in 3-D OCT images, our method can provide around 17% additional volumetric lens coverage compared with a regular imaging procedure. The proposed approach could be used clinically in early diagnosis of cortical cataract. Wider field of view offered by this method may facilitate more accurate lens biometry in its peripheral zones, which potentially contributes to understanding of lens shape modifications of the accommodating eye.