Deep Learning Methods for Image Decomposition of Cervical Cells

Tayebeh Lotfi Mahyari, R. Dansereau
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引用次数: 1

Abstract

One way to solve under-determined image decomposition is to use statistical information about the type of data to be decomposed. This information can be obtained by a deep learning where convolutional neural networks (CNN) are a subset recently used widely in image processing. In this paper, we have designed a two-stage CNN that takes cytology images of overlapped cervical cells and attempts to separate the cell images. In the first stage, we designed a CNN to segment overlapping cells. In the second stage, we designed a CNN that uses this segmentation and the original image to separate the regions. We implemented a CNN similar to U-Net for image segmentation and implemented a new network for the image separation. To train and test the proposed networks, we simulated 50000 cervical cell cytology images by overlaying individual images of real cervical cells using the Beer-Lambert law. Of these 50000 images, we used 49000 images for training and evaluated the method with 1000 test images. Results on these synthetic images give more than 97% segmentation accuracy and gives decomposition SSIM scores of more than 0.99 and PSNR score of more than 30 dB. Despite these positive results, the permutation problem that commonly effects signal separation occasionally occurred resulting in some cell structure mis-separation (for example, one cell given two nucleoli and the other given none). In addition, when the segmentation was poor from the first stage, the resulting separation was poor.
基于深度学习的宫颈细胞图像分解方法
解决欠确定图像分解的一种方法是使用关于要分解的数据类型的统计信息。这些信息可以通过深度学习获得,其中卷积神经网络(CNN)是最近在图像处理中广泛使用的一个子集。在本文中,我们设计了一个两阶段的CNN,取重叠宫颈细胞的细胞学图像,并试图分离细胞图像。在第一阶段,我们设计了一个CNN来分割重叠的细胞。在第二阶段,我们设计了一个CNN,使用这个分割和原始图像来分离区域。我们实现了一个类似于U-Net的CNN图像分割,并实现了一个新的图像分离网络。为了训练和测试所提出的网络,我们通过使用Beer-Lambert定律覆盖真实宫颈细胞的单个图像,模拟了50000个宫颈细胞细胞学图像。在这50000张图像中,我们使用49000张图像进行训练,并使用1000张测试图像对方法进行评估。结果表明,这些合成图像的分割精度在97%以上,分解SSIM分数大于0.99,PSNR分数大于30 dB。尽管有这些积极的结果,但通常影响信号分离的排列问题偶尔会发生,导致一些细胞结构错误分离(例如,一个细胞有两个核仁,而另一个没有核仁)。另外,当第一阶段分割较差时,分离效果较差。
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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