Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps

Postoperative Changes in Vessel Density according to Macular Hole and Macular Pseudohole Subtypes

Purpose: To determine the postoperative changes in vessel density according to macular hole and macular pseudohole (MPH) subtypes and to investigate the differences in the mechanisms underlying their development. We also investigated whether changes in vessel density are correlated with changes in the multifocal electroretinogram (mfERG) and best corrected visual acuity (BCVA).Methods: We reviewed the medical records of patients with MPH or a macular hole who underwent pars plana vitrectomy. We included 15 eyes of 15 patients with a full thickness macular hole (FTMH), nine eyes of nine patients with a tractional lamellar macular hole (LMH), eight eyes of eight patients with a degenerative LMH, and nine eyes of eight patients with a MPH. The BCVA, foveal avascular zone (FAZ), foveal and parafoveal vessel density, and mfERG ring 1 and ring 2 P1 amplitudes were analyzed before and 1 and 6 months after surgery.Results: One month postoperatively, the foveal vessel density of patients with a MPH or tractional LMH increased (p = 0.011, p = 0.008). The parafoveal vessel density of patients with a MPH, tractional LMH, and FTMH increased (p = 0.007, p = 0.038, p = 0.031). There was no significant increase in foveal or parafoveal vessel density in patients with a degenerative LMH (p = 0.201, p = 0.171). There was a significant correlation between the change in parafoveal vessel density and that in BCVA 6 months postoperatively in patients with a FTMH (r = -0.543, p = 0.037).Conclusions: By assessing changes in vessel density after vitrectomy, it is possible to estimate the effect of traction according to the type of macular hole. There was a significant correlation between parafoveal vessel density and BCVA in patients with a FTMH. Restoration of the retinal structure and vessel density might improve visual acuity.

Automated foveal location detection on spectral-domain optical coherence tomography in geographic atrophy patients

PURPOSE

To develop a fully automated algorithm for accurate detection of fovea location in atrophic age-related macular degeneration (AMD), based on spectral-domain optical coherence tomography (SD-OCT) scans.

METHODS

Image processing was conducted on a cohort of patients affected by geographic atrophy (GA). SD-OCT images (cube volume) from 55 eyes (51 patients) were extracted and processed with a layer segmentation algorithm to segment Ganglion Cell Layer (GCL) and Inner Plexiform Layer (IPL). Their en face thickness projection was convolved with a 2D Gaussian filter to find the global maximum, which corresponded to the detected fovea. The detection accuracy was evaluated by computing the distance between manual annotation and predicted location.

RESULTS

The mean total location error was 0.101±0.145mm; the mean error in horizontal and vertical en face axes was 0.064±0.140mm and 0.063±0.060mm, respectively. The mean error for foveal and extrafoveal retinal pigment epithelium and outer retinal atrophy (RORA) was 0.096±0.070mm and 0.107±0.212mm, respectively. Our method obtained a significantly smaller error than the fovea localization algorithm inbuilt in the OCT device (0.313±0.283mm, p <.001) or a method based on the thinnest central retinal thickness (0.843±1.221, p <.001). Significant outliers are depicted with the reliability score of the method.

CONCLUSION

Despite retinal anatomical alterations related to GA, the presented algorithm was able to detect the foveal location on SD-OCT cubes with high reliability. Such an algorithm could be useful for studying structural-functional correlations in atrophic AMD and could have further applications in different retinal pathologies.

A column-based deep learning method for the detection and quantification of atrophy associated with AMD in OCT scans

The objective quantification of retinal atrophy associated with age-related macular degeneration (AMD) is required for clinical diagnosis, follow-up, treatment efficacy evaluation, and clinical research. Spectral Domain Optical Coherence Tomography (OCT) has become an essential imaging technology to evaluate the macula. This paper describes a novel automatic method for the identification and quantification of atrophy associated with AMD in OCT scans and its visualization in the corresponding infrared imaging (IR) image. The method is based on the classification of light scattering patterns in vertical pixel-wide columns (A-scans) in OCT slices (B-scans) in which atrophy appears with a custom column-based convolutional neural network (CNN). The network classifies individual columns with 3D column patches formed by adjacent neighboring columns from the volumetric OCT scan. Subsequent atrophy columns form atrophy segments which are then projected onto the IR image and are used to identify and segment atrophy lesions in the IR image and to measure their areas and distances from the fovea. Experimental results on 106 clinical OCT scans (5,207 slices) in which cRORA atrophy (the end point of advanced dry AMD) was identified in 2,952 atrophy segments and 1,046 atrophy lesions yield a mean F₁ score of 0.78 (std 0.06) and an AUC of 0.937, both close to the observer variability. Automated computer-based detection and quantification of atrophy associated with AMD using a column-based CNN classification in OCT scans can be performed at expert level and may be a useful clinical decision support and research tool for the diagnosis, follow-up and treatment of retinal degenerations and dystrophies.

Identifying Diabetic Macular Edema and Other Retinal Diseases by Optical Coherence Tomography Image and Multiscale Deep Learning

PURPOSE

Diabetic Macular Edema has been one of the research hotspots all over the world. But as the global population continues to grow, the number of OCT images requiring manual analysis is becoming increasingly unaffordable. Medical images are often fuzzy due to the inherent physical processes of acquiring them. It is difficult for traditional algorithms to use low-quality data. And traditional algorithms usually only provide diagnostic results, which makes the reliability and interpretability of the model face challenges. To solve problem above, we proposed a more intuitive and robust diagnosis model with self-enhancement ability and clinical triage patients' ability.

METHODS

We used 38,057 OCT images (Drusen, DME, CNV and Normal) to establish and evaluate the model. All data are OCT images of fundus retina. There were 37,457 samples in the training dataset and 600 samples in the validation dataset. In order to diagnose these images accurately, we propose a multiscale transfer learning algorithm. Firstly, the sample is sent to the automatic self-enhancement module for edge detection and enhancement. Then, the processed data are sent to the image diagnosis module to determine the disease type. This process makes more data more effective and can be accurately classified. Finally, we calculated the accuracy, precision, sensitivity and specificity of the model, and verified the performance of the model from the perspective of clinical application.

RESULTS

The model proposed in this paper can provide the diagnosis results and display the detection targets more intuitively. The model reached 94.5% accuracy, 97.2% precision, 97.7% sensitivity and 97% specificity in the independent testing dataset.

CONCLUSION

Comparing the performance of relevant work and ablation test, our model achieved relatively good performance. It is proved that the model proposed in this paper has a stronger ability to recognize diseases even in the face of low-quality images. Experiment results also demonstrate its clinical referral capability. It can reduce the workload of medical staff and save the precious time of patients.