Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks

• URL: http://arxiv.org/abs/2210.02445v1
• Date: Sun, 25 Sep 2022 15:08:20 GMT
• Title: Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks
• Authors: Xiaofeng Lei, Shaohua Li, Xinxing Xu, Huazhu Fu, Yong Liu, Yih-Chung Tham, Yangqin Feng, Mingrui Tan, Yanyu Xu, Jocelyn Hui Lin Goh, Rick Siow Mong Goh, Ching-Yu Cheng
• Abstract summary: We propose a zoom-in attentive network (ZIAN) for anatomical landmark localization in ocular images. Experiments show that ZIAN achieves promising performances and outperforms state-of-the-art localization methods.
• Score: 34.05575237813503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Localizing anatomical landmarks are important tasks in medical image analysis. However, the landmarks to be localized often lack prominent visual features. Their locations are elusive and easily confused with the background, and thus precise localization highly depends on the context formed by their surrounding areas. In addition, the required precision is usually higher than segmentation and object detection tasks. Therefore, localization has its unique challenges different from segmentation or detection. In this paper, we propose a zoom-in attentive network (ZIAN) for anatomical landmark localization in ocular images. First, a coarse-to-fine, or "zoom-in" strategy is utilized to learn the contextualized features in different scales. Then, an attentive fusion module is adopted to aggregate multi-scale features, which consists of 1) a co-attention network with a multiple regions-of-interest (ROIs) scheme that learns complementary features from the multiple ROIs, 2) an attention-based fusion module which integrates the multi-ROIs features and non-ROI features. We evaluated ZIAN on two open challenge tasks, i.e., the fovea localization in fundus images and scleral spur localization in AS-OCT images. Experiments show that ZIAN achieves promising performances and outperforms state-of-the-art localization methods. The source code and trained models of ZIAN are available at https://github.com/leixiaofeng-astar/OMIA9-ZIAN.

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