Abstract
Age-related cataract is a priority eye disease, with nuclear cataract as its most common type. This paper aims for automated nuclear cataract grading based on slit-lamp photos. Different from previous efforts which rely on traditional feature extraction and grade modeling techniques, we propose in this paper a fully deep learning based solution. Given a slit-lamp photo, we localize its nuclear region by Faster R-CNN, followed by a ResNet-101 based grading model. In order to alleviate the issue of imbalanced data, a simple batch balancing strategy is introduced for improving the training of the grading network. Tested on a clinical dataset of 157 slit-lamp photos from 39 female and 31 male patients, the proposed solution outperforms the state-of-the-art, reducing the mean absolute error from 0.357 to 0.313. In addition, our solution processes a slit-lamp photo in approximately 0.1 s, which is two order faster than the state-of-the-art. With its effectiveness and efficiency, the new solution is promising for automated nuclear cataract grading.
C. Xu, X. Zhu, W. He, Y. Lu—Equal contributions.
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Acknowledgments
This work was supported by NSFC (No. 61672523, No. 81870642, No. 81670835), the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (No. 18XNLG19), Special Research Project of Intelligent Medicine, Shanghai Municipal Health Commission (2018ZHYL0220), National Key R&D Program of China (No. 2018YFC0116800) and CSC State Scholarship Fund (201806295014).
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Xu, C. et al. (2019). Fully Deep Learning for Slit-Lamp Photo Based Nuclear Cataract Grading. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_56
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DOI: https://doi.org/10.1007/978-3-030-32251-9_56
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