The accuracy of the applied technique for automated nuclei segmentation is critical in obtaining high-quality and efficient diagnostic results. Unfortunately, multiple objects in histopathological images are connected (clustered) and frequently counted as one. In this study, we present a new method for cluster splitting based on distance transform binarized with the recurrently increased threshold value and modified watershed algorithm. The proposed method treats clusters separately, splitting them into smaller sub-clusters and conclusively into separate objects, based solely on the shape feature, making it independent of the pixel intensity. The efficiency of these algorithms is validated based on the labeled set of images from two datasets: BBBC004v1 and breast cancer tissue microarrays. Results of initial nuclei detection were significantly improved by applying the proposed algorithms. Our approach outperformed the state-of-the-art techniques based on recall, precision, F1-score, and Jaccard index. The proposed method achieves very low amount of under-segmented, as well as over-segmented objects. In summary, we provide novel and efficient method for dividing the clustered nuclei in digital images of histopathological slides.

中文翻译：

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基于递归距离变换的数字病理图像簇状核分裂

用于自动核分割的应用技术的准确性对于获得高质量和高效的诊断结果至关重要。不幸的是，组织病理学图像中的多个对象被连接（聚集）并且经常被视为一个。在这项研究中，我们提出了一种基于距离变换的二元化分割方法，该距离化转换具有递归增加的阈值和改进的分水岭算法。所提出的方法单独地对待群集，仅基于形状特征将其分为较小的子群集，并最终将其分为单独的对象，从而使其独立于像素强度。基于来自两个数据集的标记图像集验证了这些算法的效率：BBBC004v1和乳腺癌组织微阵列。通过应用所提出的算法，初始核检测结果得到了显着改善。我们的方法优于基于召回率，精度，F1得分和Jaccard索引的最新技术。所提出的方法实现了非常少的分段不足和分段过多的对象。总之，我们提供了新颖有效的方法来对组织病理切片的数字图像中的聚集核进行划分。