arXiv 2309.025632023-09-05eess.IVcs.CV

使用卷积神经网络和Transformer进行全切片图像肾脏层分割评估

Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers

Vanderbilt University（范德堡大学）；Tongji University（同济大学）；Shanghai Tenth People’s Hospital（上海市第十人民医院）；Vanderbilt University Medical Center（范德堡大学医学中心）

Muhao Liu, Chenyang Qi, Shunxing Bao, Quan Liu, Ruining Deng, Yu Wang, Shilin Zhao, Haichun Yang, Yuankai Huo

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AI总结：

本文评估了六种深度学习模型（含CNN和Transformer）在小鼠肾脏全切片图像皮质层分割中的性能，发现Transformer模型总体优于CNN，为肾脏病理自动化分析提供了可行方案。

摘要：

肾脏层结构的分割，包括人肾脏全切片图像（WSI）中的皮质、外条纹、内条纹和内髓质，在肾脏病理学的自动化图像分析中起着至关重要的作用。然而，当前的手动分割过程劳动密集且难以处理大规模遇到的广泛数字病理图像。为此，数字肾脏病理学领域已出现基于深度学习的方法。然而，很少有（如果有的话）基于深度学习的方法被应用于肾脏层结构分割。针对这一空白，本文评估了在肾脏层结构分割上执行基于深度学习方法的可行性。本研究采用了具有代表性的卷积神经网络（CNN）和Transformer分割方法，包括Swin-Unet、Medical-Transformer、TransUNet、U-Net、PSPNet和DeepLabv3+。我们使用小鼠肾脏WSI对六种流行的深度学习模型在肾皮质层分割上进行了定量评估。来自我们方法的实证结果显示出令人信服的进展，以可观的平均交并比（mIoU）指数为证。结果表明，Transformer模型通常优于基于CNN的模型。通过实现对肾皮质结构的定量评估，深度学习方法有望增强这些医学专业人员做出更明智的肾脏层分割决策的能力。

英文摘要：

The segmentation of kidney layer structures, including cortex, outer stripe, inner stripe, and inner medulla within human kidney whole slide images (WSI) plays an essential role in automated image analysis in renal pathology. However, the current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images encountered at a large scale. In response, the realm of digital renal pathology has seen the emergence of deep learning-based methodologies. However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation. Addressing this gap, this paper assesses the feasibility of performing deep learning based approaches on kidney layer structure segmetnation. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches, including Swin-Unet, Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We quantitatively evaluated six prevalent deep learning models on renal cortex layer segmentation using mice kidney WSIs. The empirical results stemming from our approach exhibit compelling advancements, as evidenced by a decent Mean Intersection over Union (mIoU) index. The results demonstrate that Transformer models generally outperform CNN-based models. By enabling a quantitative evaluation of renal cortical structures, deep learning approaches are promising to empower these medical professionals to make more informed kidney 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。