BackgroundAutomatic segmentation of corneal stromal cells can assist ophthalmologists to detect abnormal morphology in confocal microscopy images, thereby assessing the virus infection or conical mutation of corneas, and avoiding irreversible pathological damage. However, the corneal stromal cells often suffer from uneven illumination and disordered vascular occlusion, resulting in inaccurate segmentation.MethodsIn response to these challenges, this study proposes a novel approach: a nnUNet and nested Transformer-based network integrated with dual high-order channel attention, named U-NTCA. Unlike nnUNet, this architecture allows for the recursive transmission of crucial contextual features and direct interaction of features across layers to improve the accuracy of cell recognition in low-quality regions. The proposed methodology involves multiple steps. Firstly, three underlying features with the same channel number are sent into an attention channel named gnConv to facilitate higher-order interaction of local context. Secondly, we leverage different layers in U-Net to integrate Transformer nested with gnConv, and concatenate multiple Transformers to transmit multi-scale features in a bottom-up manner. We encode the downsampling features, corresponding upsampling features, and low-level feature information transmitted from lower layers to model potential correlations between features of varying sizes and resolutions. These multi-scale features play a pivotal role in refining the position information and morphological details of the current layer through recursive transmission.ResultsExperimental results on a clinical dataset including 136 images show that the proposed method achieves competitive performance with a Dice score of 82.72% and an AUC (Area Under Curve) of 90.92%, which are higher than the performance of nnUNet.ConclusionThe experimental results indicate that our model provides a cost-effective and high-precision segmentation solution for corneal stromal cells, particularly in challenging image scenarios.

中文翻译：

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U-NTCA：nnUNet 和嵌套变压器，具有用于角膜细胞分割的通道注意力

背景角膜基质细胞的自动分割可以帮助眼科医生检测共焦显微镜图像中的异常形态，从而评估角膜的病毒感染或圆锥形突变，避免不可逆的病理损伤。然而，角膜基质细胞经常受到光照不均匀和血管闭塞紊乱的影响，导致分割不准确。方法针对这些挑战，本研究提出了一种新颖的方法：基于 nnUNet 和嵌套 Transformer 的网络与双高阶通道注意力集成，命名为U-NTCA。与 nnUNet 不同，该架构允许关键上下文特征的递归传输和跨层特征的直接交互，以提高低质量区域中细胞识别的准确性。所提出的方法涉及多个步骤。首先，具有相同通道号的三个底层特征被发送到名为Gn转化率促进局部环境的高阶交互。其次，我们利用 U-Net 中的不同层来集成嵌套的 TransformerGn转化率，并连接多个 Transformer 以自下而上的方式传输多尺度特征。我们对下采样特征、相应的上采样特征以及从较低层传输的低级特征信息进行编码，以对不同大小和分辨率的特征之间的潜在相关性进行建模。这些多尺度特征通过递归传输在细化当前层的位置信息和形态细节方面发挥着关键作用。结果在包括136张图像的临床数据集上的实验结果表明，该方法取得了有竞争力的性能，Dice得分为82.72%， AUC（曲线下面积）为90.92%，高于nnUNet的性能。结论实验结果表明，我们的模型为角膜基质细胞提供了一种经济高效且高精度的分割解决方案，特别是在具有挑战性的图像场景中。