Department of Histology, Embryology, Pathology and Forensic Medicine, Hue University of Medicine and Pharmacy, Hue University, Hue City, Vietnam.
Department of Radiology, Chonnam National University and Hospital, Gwangju, Korea.
Sci Rep. 2024 Apr 19;14(1):9010. doi: 10.1038/s41598-024-58506-9.
Tubular injury is the most common cause of acute kidney injury. Histopathological diagnosis may help distinguish between the different types of acute kidney injury and aid in treatment. To date, a limited number of study has used deep-learning models to assist in the histopathological diagnosis of acute kidney injury. This study aimed to perform histopathological segmentation to identify the four structures of acute renal tubular injury using deep-learning models. A segmentation model was used to classify tubule-specific injuries following cisplatin treatment. A total of 45 whole-slide images with 400 generated patches were used in the segmentation model, and 27,478 annotations were created for four classes: glomerulus, healthy tubules, necrotic tubules, and tubules with casts. A segmentation model was developed using the DeepLabV3 architecture with a MobileNetv3-Large backbone to accurately identify the four histopathological structures associated with acute renal tubular injury in PAS-stained mouse samples. In the segmentation model for four structures, the highest Intersection over Union and the Dice coefficient were obtained for the segmentation of the "glomerulus" class, followed by "necrotic tubules," "healthy tubules," and "tubules with cast" classes. The overall performance of the segmentation algorithm for all classes in the test set included an Intersection over Union of 0.7968 and a Dice coefficient of 0.8772. The Dice scores for the glomerulus, healthy tubules, necrotic tubules, and tubules with cast are 91.78 ± 11.09, 87.37 ± 4.02, 88.08 ± 6.83, and 83.64 ± 20.39%, respectively. The utilization of deep learning in a predictive model has demonstrated promising performance in accurately identifying the degree of injured renal tubules. These results may provide new opportunities for the application of the proposed methods to evaluate renal pathology more effectively.
管状损伤是急性肾损伤最常见的原因。组织病理学诊断有助于区分不同类型的急性肾损伤，并有助于治疗。迄今为止，少数研究使用深度学习模型来协助急性肾损伤的组织病理学诊断。本研究旨在使用深度学习模型对急性肾小管损伤进行组织病理学分割，以识别四种结构。使用分割模型对顺铂治疗后肾小管特异性损伤进行分类。分割模型共使用了 45 张全幻灯片图像和 400 个生成的补丁，为 4 个类别创建了 27478 个注释：肾小球、健康的肾小管、坏死的肾小管和有管型的肾小管。使用 DeepLabV3 架构和 MobileNetv3-Large 骨干开发了分割模型，以准确识别 PAS 染色的小鼠样本中与急性肾小管损伤相关的四种组织病理学结构。在用于四个结构的分割模型中，获得了“肾小球”类别的最高交并比和 Dice 系数，其次是“坏死的肾小管”、“健康的肾小管”和“有管型的肾小管”类别。在测试集中，所有类别的分割算法的整体性能包括交并比为 0.7968，Dice 系数为 0.8772。肾小球、健康肾小管、坏死肾小管和有管型的肾小管的 Dice 分数分别为 91.78 ± 11.09、87.37 ± 4.02、88.08 ± 6.83 和 83.64 ± 20.39%。深度学习在预测模型中的应用已证明在准确识别受损肾小管的程度方面具有良好的性能。这些结果可能为有效评估肾脏病理提供了新的机会。
