Adjustable Robust Transformer for High Myopia Screening in Optical Coherence Tomography

Myopia is a manifestation of visual impairment caused by an excessively elongated eyeball. Image data is critical material for studying high myopia and pathological myopia. Measurements of spherical equivalent and axial length are the gold standards for identifying high myopia, but the available image data for matching them is scarce. In addition, the criteria for defining high myopia vary from study to study, and therefore the inclusion of samples in automated screening efforts requires an appropriate assessment of interpretability. In this work, we propose a model called adjustable robust transformer (ARTran) for high myopia screening of optical coherence tomography (OCT) data. Based on vision transformer, we propose anisotropic patch embedding (APE) to capture more discriminative features of high myopia. To make the model effective under variable screening conditions, we propose an adjustable class embedding (ACE) to replace the fixed class token, which changes the output to adapt to different conditions. Considering the confusion of the data at high myopia and low myopia threshold, we introduce the label noise learning strategy and propose a shifted subspace transition matrix (SST) to enhance the robustness of the model. Besides, combining the two structures proposed above, the model can provide evidence for uncertainty evaluation. The experimental results demonstrate the effectiveness and reliability of the proposed method. Code is available at: https://github.com/maxiao0234/ARTran.

IPN-V2 and OCTA-500: Methodology and Dataset for Retinal Image Segmentation

Optical coherence tomography angiography (OCTA) is a novel imaging modality that allows a micron-level resolution to present the three-dimensional structure of the retinal vascular. In our previous work, a 3D-to-2D image projection network (IPN) was proposed for retinal vessel (RV) and foveal avascular zone (FAZ) segmentations in OCTA images. One of its advantages is that the segmentation results are directly from the original volumes without using any projection images and retinal layer segmentation. In this work, we propose image projection network V2 (IPN-V2), extending IPN by adding a plane perceptron to enhance the perceptron ability in the horizontal direction. We also propose IPN-V2+, as a supplement of the IPN-V2, by introducing a global retraining process to overcome the "checkerboard effect". Besides, we propose a new multi-modality dataset, dubbed OCTA-500. It contains 500 subjects with two field of view (FOV) types, including OCT and OCTA volumes, six types of projections, four types of text labels and two types of pixel-level labels. The dataset contains more than 360K images with a size of about 80GB. To the best of our knowledge, it is currently the largest OCTA dataset with the abundant information. Finally, we perform a thorough evaluation of the performance of IPN-V2 on the OCTA-500 dataset. The experimental results demonstrate that our proposed IPN-V2 performs better than IPN and other deep learning methods in RV segmentation and FAZ segmentation.