NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images
NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images
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DOI：
10.1016/j.compbiomed.2020.104075
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发表时间：
2021-01-01
影响因子：
7.7
通讯作者：
Kini, Jyoti
中科院分区：
文献类型：
--
作者：
Lal, Shyam;Das, Devikalyan;Kini, Jyoti
苏木精和伊红（H&E）染色组织病理学图像的细胞核分割是设计计算机辅助诊断（CAD）系统进行肿瘤诊断和预后的重要前提。自动核分割方法能够对H&E染色的组织病理学图像中的数万个核进行定性和定量分析。然而，在细胞核分割过程中，一个主要的挑战是分割可变大小的、接触的细胞核。为了解决这一挑战，我们提出了NucleiSegNet——一个强大的深度学习网络架构，用于H&E染色的肝癌组织病理学图像的细胞核分割。我们提出的架构包括三个块：鲁棒残差块、瓶颈块和注意力解码器块。鲁棒残差块是为了高效提取高级语义映射而提出的一种新方法。注意解码器块使用了一种新的注意机制来进行有效的目标定位，并通过减少误报来提高所提出架构的性能。当应用于核分割任务时，与最先进的核分割方法相比，所提出的深度学习架构产生了更好的结果。我们将我们提出的用于核分割的深度学习架构应用于来自两个数据集的一组H&E染色组织病理学图像，我们的综合结果表明，我们提出的架构优于最先进的方法。作为这项工作的一部分，我们还引入了一个新的肝脏数据集（KMC肝脏数据集），该数据集由H&E染色的肝癌组织病理学图像瓦片组成，包含80张带有细胞核注释的图像，这些图像来自印度卡纳塔克邦马尼帕尔马尼帕尔高等教育学院（MAHE），曼格洛尔Kasturba医学院（KMC）。建议的模型的源代码可在https://github.com/shyamfec/NucleiSegNet上获得。
The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is an important prerequisite in designing a computer-aided diagnostics (CAD) system for cancer diagnosis and prognosis. Automated nuclei segmentation methods enable the qualitative and quantitative analysis of tens of thousands of nuclei within H&E stained histopathology images. However, a major challenge during nuclei segmentation is the segmentation of variable sized, touching nuclei. To address this challenge, we present NucleiSegNet - a robust deep learning network architecture for the nuclei segmentation of H&E stained liver cancer histopathology images. Our proposed architecture includes three blocks: a robust residual block, a bottleneck block, and an attention decoder block. The robust residual block is a newly proposed block for the efficient extraction of high-level semantic maps. The attention decoder block uses a new attention mechanism for efficient object localization, and it improves the proposed architecture's performance by reducing false positives. When applied to nuclei segmentation tasks, the proposed deep-learning architecture yielded superior results compared to state-of-the-art nuclei segmentation methods. We applied our proposed deep learning architecture for nuclei segmentation to a set of H&E stained histopathology images from two datasets, and our comprehensive results show that our proposed architecture outperforms state-of-the-art methods. As part of this work, we also introduced a new liver dataset (KMC liver dataset) of H&E stained liver cancer histopathology image tiles, containing 80 images with annotated nuclei procured from Kasturba Medical College (KMC), Mangalore, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India. The proposed model's source code is available at https://github.com/shyamfec/NucleiSegNet.