Lal Shyam, Das Devikalyan, Alabhya Kumar, Kanfade Anirudh, Kumar Aman, Kini Jyoti
Department of E & C Engg., National Institute of Technology Karnataka, Surathkal, Mangaluru, 575025, Karnataka, India.
Department of E & C Engg., National Institute of Technology Karnataka, Surathkal, Mangaluru, 575025, Karnataka, India.
Comput Biol Med. 2021 Jan;128:104075. doi: 10.1016/j.compbiomed.2020.104075. Epub 2020 Nov 3.
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.
苏木精和伊红（H&E）染色的组织病理学图像的细胞核分割是设计用于癌症诊断和预后的计算机辅助诊断（CAD）系统的重要前提。自动化细胞核分割方法能够对H&E染色的组织病理学图像中的数万个细胞核进行定性和定量分析。然而，细胞核分割过程中的一个主要挑战是大小不一、相互接触的细胞核的分割。为应对这一挑战，我们提出了NucleiSegNet——一种用于H&E染色的肝癌组织病理学图像细胞核分割的强大深度学习网络架构。我们提出的架构包括三个模块：一个强大的残差模块、一个瓶颈模块和一个注意力解码器模块。强大的残差模块是一个新提出的模块，用于高效提取高级语义图。注意力解码器模块使用一种新的注意力机制进行高效的目标定位，并通过减少误报来提高所提出架构的性能。当应用于细胞核分割任务时，与当前最先进的细胞核分割方法相比，所提出的深度学习架构产生了更优的结果。我们将所提出的用于细胞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染色的组织病理学图像，我们的综合结果表明，我们提出的架构优于当前最先进方法。作为这项工作的一部分，我们还引入了一个新的H&E染色的肝癌组织病理学图像切片肝脏数据集（KMC肝脏数据集），其中包含从印度卡纳塔克邦芒格洛尔卡斯图尔巴医学院（KMC）、马尼帕尔高等教育学院（MAHE）、马尼帕尔采集的80张带有注释细胞核的图像。所提出模型的源代码可在https://github.com/shyamfec/NucleiSegNet获取。
