Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, Uttarkhand, India.
Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Dharwad, Karnataka, 580009, India.
J Digit Imaging. 2023 Aug;36(4):1633-1642. doi: 10.1007/s10278-022-00764-y. Epub 2023 Apr 5.
Digital pathology is vital for the correct diagnosis of kidney before transplantation or kidney disease identification. One of the key challenges in kidney diagnosis is glomerulus detection in kidney tissue segments. In this study, we propose a deep learning-based method for glomerulus detection from digitized kidney slide segments. The proposed method applies models based on convolutional neural networks to detect image segments containing the glomerulus region. We employ various networks such as ResNets, UNet, LinkNet, and EfficientNet to train the models. In our experiments on a network trained on the NIH HuBMAP kidney whole slide image dataset, the proposed method achieves the highest scores with Dice coefficient of 0.942.
数字病理学对于肾脏移植前的正确诊断或肾脏疾病的识别至关重要。肾脏诊断中的一个关键挑战是在肾脏组织切片中检测肾小球。在这项研究中，我们提出了一种基于深度学习的方法，用于从数字化肾脏切片中检测肾小球。所提出的方法应用基于卷积神经网络的模型来检测包含肾小球区域的图像段。我们使用各种网络，如 ResNets、UNet、LinkNet 和 EfficientNet 来训练模型。在我们对在 NIH HuBMAP 肾脏全幻灯片图像数据集上训练的网络进行的实验中，所提出的方法在 Dice 系数为 0.942 时取得了最高得分。
