School of Information Science and Technology, North China University of Technology.
Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing 100144, China.
Bioinformatics. 2020 May 1;36(10):3225-3233. doi: 10.1093/bioinformatics/btaa107.
For the diagnosis of cancer, manually counting nuclei on massive histopathological images is tedious and the counting results might vary due to the subjective nature of the operation.
This paper presents a new segmentation and counting method for nuclei, which can automatically provide nucleus counting results. This method segments nuclei with detected nuclei seed markers through a modified simple one-pass superpixel segmentation method. Rather than using a single pixel as a seed, we created a superseed for each nucleus to involve more information for improved segmentation results. Nucleus pixels are extracted by a newly proposed fusing method to reduce stain variations and preserve nucleus contour information. By evaluating segmentation results, the proposed method was compared to five existing methods on a dataset with 52 immunohistochemically (IHC) stained images. Our proposed method produced the highest mean F1-score of 0.668. By evaluating the counting results, another dataset with more than 30 000 IHC stained nuclei in 88 images were prepared. The correlation between automatically generated nucleus counting results and manual nucleus counting results was up to R2 = 0.901 (P < 0.001). By evaluating segmentation results of proposed method-based tool, we tested on a 2018 Data Science Bowl (DSB) competition dataset, three users obtained DSB score of 0.331 ± 0.006.
The proposed method has been implemented as a plugin tool in ImageJ and the source code can be freely downloaded.
Supplementary data are available at Bioinformatics online.
在癌症诊断中，手动在大量组织病理学图像上计数细胞核既繁琐，又可能因操作的主观性而导致计数结果不同。
本文提出了一种新的细胞核分割和计数方法，它可以自动提供细胞核计数结果。该方法通过改进的简单单遍超像素分割方法，使用检测到的细胞核种子标记来分割细胞核。我们不是用单个像素作为种子，而是为每个细胞核创建一个超种子，以包含更多信息来提高分割效果。细胞核像素通过新提出的融合方法提取，以减少染色变化并保留细胞核轮廓信息。通过评估分割结果，我们在包含 52 张免疫组织化学（IHC）染色图像的数据集上将所提出的方法与五种现有方法进行了比较。我们提出的方法产生了最高的平均 F1 分数 0.668。通过评估计数结果，我们准备了另外一个包含 88 张图像中超过 30000 个 IHC 染色细胞核的数据集。自动生成的细胞核计数结果与手动细胞核计数结果之间的相关性高达 R2=0.901（P<0.001）。通过在 2018 年数据科学碗（DSB）竞赛数据集上评估基于所提出的方法的工具的分割结果，三名用户获得了 0.331±0.006 的 DSB 得分。
所提出的方法已作为 ImageJ 的插件工具实现，并且可以免费下载源代码。
补充数据可在生物信息学在线获得。
