Mr. Amday Wolde Profile

Amday Wolde

at National Eye Institute

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Proceedings Article | 4 April 2022 Poster + Paper

Hybrid transformer for lesion segmentation on adaptive optics retinal images

Jianfei Liu, Joanne Li, Amday Wolde, Catherine Cukras, Johnny Tam

Proceedings Volume 12033, 120331W (2022) https://doi.org/10.1117/12.2612379

KEYWORDS: Image segmentation, Transformers, Adaptive optics, Computer programming, Convolution, Retinal scanning, Medical imaging, Convolutional neural networks, Tissues, Eye

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Adaptive optics (AO) retinal imaging has enabled the visualization of cellular-level changes in the living human eye. However, imaging tissue-level lesions with such high resolution introduces unique challenges. At a fine spatial scale, intralesion features can resemble cells, effectively serving as camouflage and making it difficult to delineate the boundary of lesions. The size discrepancy between the tissue-level lesions and retinal cells is also highly variable, ranging from a difference of several-fold to greater than an order-of-magnitude. Here, we introduce a hybrid-transformer based on the combination of a convolutional LinkNet and a fully axial attention transformer network to consider both local and global image features, which excels at identifying tissue-level lesions within a cellular landscape. After training the hybrid transformer on 489 manually-annotated AO images, accurate lesion segmentation was achieved on a separate test dataset consisting of 75 AO images for validation. The segmentation accuracy achieved using the hybrid transformer was superior to the use of convolutional neural networks alone (U-Net and LinkNet) or transformer-based networks alone (AxialDeepLab and Medical Transformer) (p<0.05). These experimental results demonstrate that the combination of convolution and transformer networks are an efficient way to utilize both local and global image features for the purpose of lesion segmentation in medical imaging and may be important for computer-aided diagnosis that relies on accurate lesion segmentation.

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