Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks
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.
Keywords
Cite
@article{arxiv.2210.02445,
title = {Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks},
author = {Xiaofeng Lei and Shaohua Li and Xinxing Xu and Huazhu Fu and Yong Liu and Yih-Chung Tham and Yangqin Feng and Mingrui Tan and Yanyu Xu and Jocelyn Hui Lin Goh and Rick Siow Mong Goh and Ching-Yu Cheng},
journal= {arXiv preprint arXiv:2210.02445},
year = {2022}
}