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

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

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.

基于卷积神经网络和变压器的全切片肾层分割评价。
人体肾脏全切片图像(WSI)对肾脏皮层、外条纹、内条纹和内髓质等层结构的分割在肾脏病理自动图像分析中起着至关重要的作用。然而,目前的人工分割过程被证明是劳动密集型的,对于处理大规模遇到的大量数字病理图像是不可行的。作为回应,数字肾脏病理学领域已经出现了基于深度学习的方法。然而,基于深度学习的方法很少(如果有的话)被应用于肾层结构分割。为了解决这一问题,本文评估了在肾层结构分割上执行基于深度学习方法的可行性。本研究采用代表性的卷积神经网络(CNN)和Transformer分割方法,包括swwin - unet、Medical-Transformer、TransUNet、U-Net、PSPNet和DeepLabv3+。我们定量评估了六种流行的深度学习模型在小鼠肾wsi肾皮质层分割上的应用。我们的方法产生的实证结果显示出令人信服的进步,正如一个体面的平均交叉口联盟(mIoU)指数所证明的那样。结果表明,Transformer模型总体上优于基于cnn的模型。通过对肾皮质结构进行定量评估,深度学习方法有望使这些医疗专业人员能够做出更明智的肾层分割。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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