Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 41, Issue 2, Pages 394-406Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2021.3112716
Keywords
Image segmentation; Retina; Feature extraction; Transformers; Task analysis; Interference; Decoding; Optical coherence tomography; drusen; transformer; segmentation
Categories
Funding
- National Key Research and Development Program of China [2018YFA0701700]
- National Nature Science Foundation of China [U20A20170, 61622114, 81401472]
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In this paper, a novel multi-scale transformer global attention network (MsTGANet) is proposed for drusen segmentation in retinal OCT images. The network utilizes a multi-scale transformer non-local module and a multi-semantic global channel and spatial joint attention module to improve segmentation accuracy.
Drusen is considered as the landmark for diagnosis of AMD and important risk factor for the development of AMD. Therefore, accurate segmentation of drusen in retinal OCT images is crucial for early diagnosis of AMD. However, drusen segmentation in retinal OCT images is still very challenging due to the large variations in size and shape of drusen, blurred boundaries, and speckle noise interference. Moreover, the lack of OCT dataset with pixel-level annotation is also a vital factor hindering the improvement of drusen segmentation accuracy. To solve these problems, a novel multi-scale transformer global attention network (MsTGANet) is proposed for drusen segmentation in retinal OCT images. In MsTGANet, which is based on U-Shape architecture, a novel multi-scale transformer non-local (MsTNL) module is designed and inserted into the top of encoder path, aiming at capturing multi-scale non-local features with long-range dependencies from different layers of encoder. Meanwhile, a novel multi-semantic global channel and spatial joint attention module (MsGCS) between encoder and decoder is proposed to guide the model to fuse different semantic features, thereby improving the model's ability to learn multi-semantic global contextual information. Furthermore, to alleviate the shortage of labeled data, we propose a novel semi-supervised version of MsTGANet (Semi-MsTGANet) based on pseudo-labeled data augmentation strategy, which can leverage a large amount of unlabeled data to further improve the segmentation performance. Finally, comprehensive experiments are conducted to evaluate the performance of the proposed MsTGANet and Semi-MsTGANet. The experimental results show that our proposed methods achieve better segmentation accuracy than other state-of-the-art CNN-based methods.
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