基于深度U-Net和其他版本U-Net模型的人类细胞核语义分割。

Network (Bristol, England) Pub Date : 2022-08-01 Epub Date: 2022-07-12 DOI:10.1080/0954898X.2022.2096938
Yadavendra, Satish Chand
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引用次数: 1

摘要

深度学习模型在许多领域发挥着至关重要的作用,包括医学图像分析。这些模型在没有人为干预的情况下提取重要特征。在本文中,我们提出了一种深度卷积神经网络,称为深度U-Net模型,用于细胞核的分割,细胞核是决定身体功能和结构的关键功能单元。细胞核中含有各种DNA、RNA、染色体以及支配一切生命活动的基因,它的紊乱可能导致不同类型的疾病,如癌症、心脏病、糖尿病、阿尔茨海默病等。如果正确了解细胞核结构,由细胞核紊乱引起的疾病可能会及早发现。如果知道细胞核的形状和大小,也可能减少药物发现的时间。我们在Kaggle主办的2018年数据科学碗比赛中使用的核分割数据集上评估了所提出模型的性能。我们将其性能与U-Net、Attention U-Net、R2U-Net、Attention R2U-Net以及两个版本的U-Net++在有和没有监督的情况下,在损失、骰子系数、骰子损失、交联和精度方面进行比较。我们的模型比现有的模型性能更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic segmentation of human cell nucleus using deep U-Net and other versions of U-Net models.

The deep learning models play an essential role in many areas, including medical image analysis. These models extract important features without human intervention. In this paper, we propose a deep convolution neural network, named as deep U-Net model, for the segmentation of the cell nucleus, a critical functional unit that determines the function and structure of the body. The nucleus contains all kinds of DNA, RNA, chromosomes, and genes governing all life activities, and its disorder may lead to different types of diseases such as cancer, heart disease, diabetes, Alzheimer's, etc. If the nucleus structure is known correctly, diseases due to nucleus disorder may be detected early. It may also reduce the drug discovery time if the shape and size of the nucleus are known. We evaluate the performance of the proposed models on the nucleus segmentation data set used by the Data Science Bowl 2018 competition hosted by Kaggle. We compare its performance with that of the U-Net, Attention U-Net, R2U-Net, Attention R2U-Net, and both versions of the U-Net++ with and without supervision, in terms of loss, dice coefficient, dice loss, intersection over union, and accuracy. Our model performs better than the existing models.

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