Segmentation of Heavily Clustered Nuclei from Histopathological Images
Segmentation of Heavily Clustered Nuclei from Histopathological Images
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DOI：
10.1038/s41598-019-38813-2
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发表时间：
2019-03-14
影响因子：
4.6
通讯作者：
Johnson, Sarah J.
中科院分区：
文献类型：
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作者：
Abdolhoseini, Mahmoud;Kluge, Murielle G.;Johnson, Sarah J.
自动细胞核分割是进一步了解细胞特征和功能的关键，它支持计算机辅助病理学在乳腺癌和脑肿瘤等疾病的早期诊断。尽管在自动分割方面取得了相当大的进步，但由于噪声和污渍吸收不均匀引起的强度变化，分割密集聚集的核仍然是一项具有挑战性的任务。为了解决这个问题，我们提出了一种适用于不同蛋白质染色的各种组织病理图像的新方法，具有高速，准确和自动化水平。该算法采用了一种新的局部自适应阈值分割方法。然后提出了一种基于多级阈值分割和分水岭分割的聚类核分离方法。通过基于模型的合并步骤消除过度分割和基于模型的校正步骤改善分割结果并消除小目标。我们将我们的方法应用于三个图像数据集：用于苏木精和伊红（H&E）染色的乳腺癌，用于标记细胞核的DNA染色的果蝇Kc167细胞，以及用于NeuN染色的成熟神经元。评估结果表明，我们的方法优于最先进的方法在准确性，精密度，fl测量，和计算时间。
Automated cell nucleus segmentation is the key to gain further insight into cell features and functionality which support computer-aided pathology in early diagnosis of diseases such as breast cancer and brain tumour. Despite considerable advances in automated segmentation, it still remains a challenging task to split heavily clustered nuclei due to intensity variations caused by noise and uneven absorption of stains. To address this problem, we propose a novel method applicable to variety of histopathological images stained for different proteins, with high speed, accuracy and level of automation. Our algorithm is initiated by applying a new locally adaptive thresholding method on watershed regions. Followed by a new splitting technique based on multilevel thresholding and the watershed algorithm to separate clustered nuclei. Finalized by a model-based merging step to eliminate oversegmentation and a model-based correction step to improve segmentation results and eliminate small objects. We have applied our method to three image datasets: breast cancer stained for hematoxylin and eosin (H&E), Drosophila Kc167 cells stained for DNA to label nuclei, and mature neurons stained for NeuN. Evaluated results show our method outperforms the state-of-the-art methods in terms of accuracy, precision, Fl-measure, and computational time.