Automatic Segmentation of Macular Edema in Retinal OCT Images Using Improved U-Net++

Segmentation of paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography images through weakly supervised deep convolutional networks

BACKGROUND AND OBJECTIVES

Spectral-domain optical coherence tomography (SD-OCT) is a valuable tool for non-invasive imaging of the retina, allowing the discovery and visualization of localized lesions, the presence of which is associated with eye diseases. The present study introduces X-Net, a weakly supervised deep-learning framework for automated segmentation of paracentral acute middle maculopathy (PAMM) lesions in retinal SD-OCT images. Despite recent advances in the development of automatic methods for clinical analysis of OCT scans, there remains a scarcity of studies focusing on the automated detection of small retinal focal lesions. Additionally, most existing solutions depend on supervised learning, which can be time-consuming and require extensive image labeling, whereas X-Net offers a solution to these challenges. As far as we can determine, no prior study has addressed the segmentation of PAMM lesions in SD-OCT images.

METHODS

This study leverages 133 SD-OCT retinal images, each containing instances of paracentral acute middle maculopathy lesions. A team of eye experts annotated the PAMM lesions in these images using bounding boxes. Then, labeled data were used to train a U-Net that performs pre-segmentation, producing region labels of pixel-level accuracy. To attain a highly-accurate final segmentation, we introduced X-Net, a novel neural network made up of a master and a slave U-Net. During training, it takes the expert annotated, and pixel-level pre-segment annotated images and employs sophisticated strategies to ensure the highest segmentation accuracy.

RESULTS

The proposed method was rigorously evaluated on clinical retinal images excluded from training and achieved an accuracy of 99% with a high level of similarity between the automatic segmentation and expert annotation, as demonstrated by a mean Intersection-over-Union of 0.8. Alternative methods were tested on the same data. Single-stage neural networks proved insufficient for achieving satisfactory results, confirming that more advanced solutions, such as the proposed method, are necessary. We also found that X-Net using Attention U-net for both the pre-segmentation and X-Net arms for the final segmentation shows comparable performance to the proposed method, suggesting that the proposed approach remains a viable solution even when implemented with variants of the classic U-Net.

CONCLUSIONS

The proposed method exhibits reasonably high performance, validated through quantitative and qualitative evaluations. Medical eye specialists have also verified its validity and accuracy. Thus, it could be a viable tool in the clinical assessment of the retina. Additionally, the demonstrated approach for annotating the training set has proven to be effective in reducing the expert workload.

Automatic segmentation of multitype retinal fluid from optical coherence tomography images using semisupervised deep learning network

BACKGROUND/AIMS

To develop and validate a deep learning model for automated segmentation of multitype retinal fluid using optical coherence tomography (OCT) images.

METHODS

We retrospectively collected a total of 2814 completely anonymised OCT images with subretinal fluid (SRF) and intraretinal fluid (IRF) from 141 patients between July 2018 and June 2020, constituting our in-house retinal OCT dataset. On this dataset, we developed a novel semisupervised retinal fluid segmentation deep network (Ref-Net) to automatically identify SRF and IRF in a coarse-to-refine fashion. We performed quantitative and qualitative analyses on the model's performance while verifying its generalisation ability by using our in-house retinal OCT dataset for training and an unseen Kermany dataset for testing. We also determined the importance of major components in the semisupervised Ref-Net through extensive ablation. The main outcome measures were Dice similarity coefficient (Dice), sensitivity (Sen), specificity (Spe) and mean absolute error (MAE).

RESULTS

Our model trained on a handful of labelled OCT images manifested higher performance (Dice: 81.2%, Sen: 87.3%, Spe: 98.8% and MAE: 1.1% for SRF; Dice: 78.0%, Sen: 83.6%, Spe: 99.3% and MAE: 0.5% for IRF) over most cutting-edge segmentation models. It obtained expert-level performance with only 80 labelled OCT images and even exceeded two out of three ophthalmologists with 160 labelled OCT images. Its satisfactory generalisation capability across an unseen dataset was also demonstrated.

CONCLUSION

The semisupervised Ref-Net required only la few labelled OCT images to generate outstanding performance in automate segmentation of multitype retinal fluid, which has the potential for providing assistance for clinicians in the management of ocular disease.

Research on Semantic Segmentation Method of Macular Edema in Retinal OCT Images Based on Improved Swin-Unet

Optical coherence tomography (OCT), as a new type of tomography technology, has the characteristics of non-invasive, real-time imaging and high sensitivity, and is currently an important medical imaging tool to assist ophthalmologists in the screening, diagnosis, and follow-up treatment of patients with macular disease. In order to solve the problem of irregular occurrence area of diabetic retinopathy macular edema (DME), multi-scale and multi-region cluster of macular edema, which leads to inaccurate segmentation of the edema area, an improved Swin-Unet networks model was proposed for automatic semantic segmentation of macular edema lesion areas in OCT images. Firstly, in the deep bottleneck of the Swin-Unet network, the Resnet network layer was used to increase the extraction of pairs of sub-feature images. Secondly, the Swin Transformer block and skip connection structure were used for global and local learning, and the regions after semantic segmentation were morphologically smoothed and post-processed. Finally, the proposed method was performed on the macular edema patient dataset publicly available at Duke University, and was compared with previous segmentation methods. The experimental results show that the proposed method can not only improve the overall semantic segmentation accuracy of retinal macular edema, but also further to improve the semantic segmentation effect of multi-scale and multi-region edema regions.