SiliCoN：组织学图像的同步细胞核分割与颜色归一化
SiliCoN: Simultaneous Nuclei Segmentation and Color Normalization of Histological Images
AI总结：
本文提出深度生成模型 SiliCoN，利用截断正态混合先验与空间注意力实现组织学图像细胞核分割和颜色归一化的同步解耦学习。
摘要：
从组织学图像中分割细胞核区域是组织学图像自动化计算机辅助分析的一项重要任务，尤其是在染色组织图像的颜色外观存在不可接受的颜色变化时。虽然颜色归一化能够实现更好的细胞核分割，但准确分割细胞核结构也会使颜色归一化变得相当简单。在此方面，本文提出了一种新颖的深度生成模型，用于同时分割细胞核结构并对染色组织学图像的颜色外观进行归一化。该模型明智地融合了截断正态分布和空间注意力的优点。模型假设与特定组织学图像对应的潜在颜色外观信息独立于相应的细胞核分割图以及嵌入图信息。这种解耦表示使模型具有泛化性和适应性，因为颜色外观信息的修改或丢失不会影响细胞核分割图以及嵌入信息。此外，为处理相关组织化学试剂的染色重叠，潜在颜色外观代码的先验被假设为截断正态分布的混合。所提出的模型结合了空间注意力概念，用于从组织学图像中分割细胞核区域。所提方法的性能，以及与相关最先进算法的比较分析，已在公开可用的标准组织学图像数据集上得到展示。
英文摘要：
Segmentation of nuclei regions from histological images is an important task for automated computer-aided analysis of histological images, particularly in the presence of impermissible color variation in the color appearance of stained tissue images. While color normalization enables better nuclei segmentation, accurate segmentation of nuclei structures makes color normalization rather trivial. In this respect, the paper proposes a novel deep generative model for simultaneously segmenting nuclei structures and normalizing color appearance of stained histological images.This model judiciously integrates the merits of truncated normal distribution and spatial attention. The model assumes that the latent color appearance information, corresponding to a particular histological image, is independent of respective nuclei segmentation map as well as embedding map information. The disentangled representation makes the model generalizable and adaptable as the modification or loss in color appearance information cannot be able to affect the nuclei segmentation map as well as embedding information. Also, for dealing with the stain overlap of associated histochemical reagents, the prior for latent color appearance code is assumed to be a mixture of truncated normal distributions. The proposed model incorporates the concept of spatial attention for segmentation of nuclei regions from histological images. The performance of the proposed approach, along with a comparative analysis with related state-of-the-art algorithms, has been demonstrated on publicly available standard histological image data sets.
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