Department of Precision Machinery and Instruments, University of Science and Technology of China, Hefei, Anhui, 230026, PR China; School of Biomedical Engineering, Division of Life and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, PR China; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, PR China.
Department of Precision Machinery and Instruments, University of Science and Technology of China, Hefei, Anhui, 230026, PR China.
Comput Biol Med. 2024 May;174:108458. doi: 10.1016/j.compbiomed.2024.108458. Epub 2024 Apr 16.
Macular edema, a prevalent ocular complication observed in various retinal diseases, can lead to significant vision loss or blindness, necessitating accurate and timely diagnosis. Despite the potential of deep learning for segmentation of macular edema, challenges persist in accurately identifying lesion boundaries, especially in low-contrast and noisy regions, and in distinguishing between Inner Retinal Fluid (IRF), Sub-Retinal Fluid (SRF), and Pigment Epithelial Detachment (PED) lesions. To address these challenges, we present a novel approach, termed Semantic Uncertainty Guided Cross-Transformer Network (SuGCTNet), for the simultaneous segmentation of multi-class macular edema. Our proposed method comprises two key components, the semantic uncertainty guided attention module (SuGAM) and the Cross-Transformer module (CTM). The SuGAM module utilizes semantic uncertainty to allocate additional attention to regions with semantic ambiguity, improves the segmentation performance of these challenging areas. On the other hand, the CTM module capitalizes on both uncertainty information and multi-scale image features to enhance the overall continuity of the segmentation process, effectively minimizing feature confusion among different lesion types. Rigorous evaluation on public datasets and various OCT imaging device data demonstrates the superior performance of our proposed method compared to state-of-the-art approaches, highlighting its potential as a valuable tool for improving the accuracy and reproducibility of macular edema segmentation in clinical settings, and ultimately aiding in the early detection and diagnosis of macular edema-related diseases and associated retinal conditions.
黄斑水肿是一种常见的眼部并发症，存在于多种视网膜疾病中，可导致严重的视力丧失甚至失明，因此需要进行准确和及时的诊断。尽管深度学习在黄斑水肿分割方面具有潜力，但在准确识别病变边界方面仍然存在挑战，尤其是在低对比度和噪声区域，并且在区分内视网膜液（IRF）、视网膜下液（SRF）和色素上皮脱离（PED）病变方面存在挑战。为了解决这些挑战，我们提出了一种新的方法，称为语义不确定性引导交叉变换网络（SuGCTNet），用于同时分割多类黄斑水肿。我们提出的方法包括两个关键组件，语义不确定性引导注意力模块（SuGAM）和交叉变换模块（CTM）。SuGAM 模块利用语义不确定性为具有语义模糊性的区域分配额外的注意力，从而提高这些具有挑战性区域的分割性能。另一方面，CTM 模块利用不确定性信息和多尺度图像特征来增强分割过程的整体连续性，有效地最小化不同病变类型之间的特征混淆。在公共数据集和各种 OCT 成像设备数据上的严格评估表明，与最先进的方法相比，我们提出的方法具有优越的性能，这表明它有潜力成为提高临床环境中黄斑水肿分割准确性和可重复性的有价值工具，最终有助于早期检测和诊断与黄斑水肿相关的疾病和相关的视网膜状况。
