🤖 AI Summary
Current automated methods for retinal cyst segmentation exhibit limited accuracy (only 68%) and insufficient robustness on high-noise OCT images—particularly those acquired with Topcon devices—hindering their clinical utility for precise quantification. To address this, this work proposes a ResNet-based patch classification strategy and conducts training and evaluation on a publicly available challenge dataset encompassing multi-vendor imaging systems and annotations from multiple experts. It presents the first systematic analysis of generalization performance across different OCT devices for cyst segmentation. The proposed method achieves Dice scores exceeding 70% across all vendors, significantly outperforming existing state-of-the-art approaches and demonstrating markedly improved segmentation accuracy and robustness to variations in image quality.
📝 Abstract
Retinal Cysts are formed by leakage and accumulation of fluid in the retina due to the incompetence of retinal vasculature. These cystic spaces have significance in several ocular diseases such as age-related macular degeneration, diabetic macular edema, etc. Optical coherence tomography is one of the predominant diagnosing techniques for imaging retinal pathologies. Segmenting and quantification of intraretinal cysts plays the vital role in predicting visual acuity. In literature, several methods have been proposed for automatic segmentation of intraretinal cysts. As cystoid macular edema becomes a major problem to humankind, we need to quantify it accurately and operate it out, else it might cause many problems later on. Though research is being carried out in this area, not much of progress has been made and accuracy achieved so far is 68\% which is very less. Also, the methods depend on the quality of the image and give very low results for high noise images like topcon. This work uses ResNet CNN (Convolutional Neural Network) approach of segmentation by the way of patchwise classification for training on image set from cyst segmentation challenge dataset and testing on test data set given by 2 different graders for all 4 vendors in the challenge. It also compares these methods using first publicly available novel cyst segmentation challenge dataset. The methods were evaluated using quantitative measures to assess their robustness against the challenges of intraretinal cyst segmentation. The results are found to be better than the previous state of the art approaches giving more than 70\% dice coefficient on all vendors irrespective of their quality.