arXiv 2408.092782025-03-21eess.IVcs.CV

用于增强肾脏病理分层分割的跨物种数据集成

Cross-Species Data Integration for Enhanced Layer Segmentation in Kidney Pathology

Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Data Science Institute, Vanderbilt University（范德堡大学数据科学研究所）

Junchao Zhu, Mengmeng Yin, Ruining Deng, Yitian Long, Yu Wang, Yaohong Wang, Shilin Zhao, Haichun Yang, Yuankai Huo

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AI总结：

针对临床肾脏病理标注数据稀缺的问题，提出将与人类肾脏结构相似的小鼠PAS染色数据与人类数据联合训练，显著提升CNN和Transformer架构分割模型的mIoU与Dice得分及泛化能力。

摘要：

肾皮质与髓质边界的精准勾勒对后续功能结构分析和疾病诊断至关重要。训练高质量的分层分割深度学习模型依赖大量标注数据的可用性。然而，由于医疗数据涉及患者隐私且临床病例稀缺，从临床来源构建病理数据集难度较大且成本高昂。此外，使用外部自然图像数据集会在域泛化过程中引入噪声。跨物种同源数据（例如小鼠肾脏数据，其与人类肾脏具有高度结构和特征相似性）具备提升模型在人类数据集上性能的潜力。本研究将收集的私有过碘酸-希夫（Periodic Acid-Schiff, PAS）染色小鼠肾脏数据集纳入人类肾脏数据集进行联合训练。结果表明，引入跨物种同源数据后，基于CNN和Transformer架构的语义分割模型在人类肾皮质与髓质数据集上的平均交并比（mIoU）分别提升1.77%和1.24%，Dice系数分别提升1.76%和0.89%。该方法还能够增强模型的泛化能力。这表明跨物种同源数据作为一种低噪声可训练数据源，能够在临床样本有限的条件下帮助提升模型性能。代码可在https://github.com/hrlblab/layer_segmentation获取。

英文摘要：

Accurate delineation of the boundaries between the renal cortex and medulla is crucial for subsequent functional structural analysis and disease diagnosis. Training high-quality deep-learning models for layer segmentation relies on the availability of large amounts of annotated data. However, due to the patient's privacy of medical data and scarce clinical cases, constructing pathological datasets from clinical sources is relatively difficult and expensive. Moreover, using external natural image datasets introduces noise during the domain generalization process. Cross-species homologous data, such as mouse kidney data, which exhibits high structural and feature similarity to human kidneys, has the potential to enhance model performance on human datasets. In this study, we incorporated the collected private Periodic Acid-Schiff (PAS) stained mouse kidney dataset into the human kidney dataset for joint training. The results showed that after introducing cross-species homologous data, the semantic segmentation models based on CNN and Transformer architectures achieved an average increase of 1.77% and 1.24% in mIoU, and 1.76% and 0.89% in Dice score for the human renal cortex and medulla datasets, respectively. This approach is also capable of enhancing the model's generalization ability. This indicates that cross-species homologous data, as a low-noise trainable data source, can help improve model performance under conditions of limited clinical samples. Code is available at https://github.com/hrlblab/layer_segmentation.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。