生成式深度学习用于未注册数字病理图像的计算脱色和复染
Generative Deep Learning for Computational Destaining and Restaining of Unregistered Digital Pathology Images
AI总结：
本文提出基于cGAN的生成式深度学习方法，用于未注册数字病理图像的计算脱色和复染，通过预处理流程提升模型性能，验证了在跨机构数据集上的可行性。
摘要：
条件生成对抗网络（cGANs）已实现了数字病理全切片图像（WSI）中血清素和伊红（H&E）的高保真计算染色和脱色。然而，其在无重新训练情况下跨机构WSI的泛化能力仍不明确。先前开发的cGAN模型在102个注册的前列腺核心活检WSI上进行评估，测试了82个空间未注册的WSI。为避免域偏移而无需重新训练，开发了包含基于直方图的H&E染色WSI标准化和通道级强度校准的预处理流程。由于图像注册在实际部署中被有意省略，报告的定量结果是保守的下限，反映了模型性能和有限的空间对齐。在这些条件下，虚拟脱色实现了皮尔逊相关系数（PCC）为0.854，结构相似性指数测量（SSIM）为0.699，峰值信噪比（PSNR）为18.41 dB。从计算脱色输出进行H&E复染在所有指标上均优于直接从地面真实未染色输入进行染色（PCC: 0.798 vs. 0.715; SSIM: 0.756 vs. 0.718; PSNR: 20.08 vs. 18.51 dB），表明预处理质量可能比模型容量更具限制性。定性病理审查显示良性腺体结构的保留，同时恶性腺体常呈现血管状形态。这些发现支持了应用基于cGAN的计算H&E染色和脱色生成模型到外部WSI数据集的可行性，仅通过预处理适应，同时定义特定形态学目标以未来域适应。
英文摘要：
Conditional generative adversarial networks (cGANs) have enabled high-fidelity computational staining and destaining of hematoxylin and eosin (H&E) in digital pathology whole-slide images (WSI). However, their ability to generalize to out-of-distribution WSI across institutions without retraining remains insufficiently characterized. Previously developed cGAN models trained on 102 registered prostate core biopsy WSIs from Brigham and Women's Hospital were evaluated on 82 spatially unregistered WSIs acquired at Stanford University. To mitigate domain shift without retraining, a preprocessing pipeline consisting of histogram-based stain normalization for H&E-stained WSIs and channel-wise intensity calibration for unstained WSIs was developed. Because image registration was intentionally omitted for real-world deployment conditions, the reported quantitative results are conservative lower bounds reflecting both model performance and limited spatial alignment. Under these conditions, virtual destaining achieved a Pearson correlation coefficient (PCC) of 0.854, structural similarity index measure (SSIM) of 0.699, and peak signal-to-noise ratio (PSNR) of 18.41 dB. H&E restaining from computationally destained outputs outperformed direct staining from ground-truth unstained inputs across all metrics (PCC: 0.798 vs. 0.715; SSIM: 0.756 vs. 0.718; PSNR: 20.08 vs. 18.51 dB), suggesting that preprocessing quality may be more limiting than model capacity. Qualitative pathological review indicated preservation of benign glandular structures while showing that malignant glands were often rendered with vessel-like morphologies. These findings support the feasibility of applying cGAN-based computational H&E staining and destaining generative models to external WSI datasets using preprocessing-based adaptation alone while defining specific morphological targets for future domain adaptation.
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