bibtext: @InProceedings{ Zho_Refining_MICCAI2024,
                   author = { Zhou, Qian and Zou, Hua and Wang, Zhongyuan and Jiang, Haifeng and Wang, Yong },
                   title = { { Refining Intraocular Lens Power Calculation: A Multi-modal Framework Using Cross-layer Attention and Effective Channel Attention } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: Selecting the appropriate power for intraocular lenses (IOLs) is crucial for the success of cataract surgeries. Traditionally, ophthalmologists rely on manually designed formulas like “Barrett” and “Hoffer Q” to calculate IOL power. However, these methods exhibit limited accuracy since they primarily focus on biometric data such as axial length and corneal curvature, overlooking the rich details in preoperative images that reveal the eye’s internal anatomy. In this study, we propose a novel deep learning model that leverages multi-modal information for accurate IOL power calculation. In particular, to address the low information density in optical coherence tomography (OCT) images (i.e., most regions are with zero pixel values), we introduce a cross-layer attention module to take full advantage of hierarchical contextual information to extract comprehensive anatomical features. Additionally, the IOL powers given by traditional formulas are taken as prior knowledge to benefit model training. The proposed method is evaluated on a self-collected dataset consisting of 174 samples and compared with other approaches. The experimental results demonstrate that our approach significantly surpasses competing methods, achieving a mean absolute error of just 0.367 diopters (D). Impressively, the percentage of eyes with a prediction error within ± 0.5 D achieves 84.1%. Furthermore, extensive ablation studies are conducted to validate each component’s contribution and identify the biometric parameters most relevant to accurate IOL power calculation. Codes will be available at https://github.com/liyiersan/IOL.
title: Refining Intraocular Lens Power Calculation: A Multi-modal Framework Using Cross-layer Attention and Effective Channel Attention
authors: Zhou, Qian Zou, Hua Wang, Zhongyuan Jiang, Haifeng Wang, Yong