Digital pathology is one of the most significant developments in modern medicine. Pathological examinations are the gold standard of medical protocols and play a fundamental role in diagnosis. Recently, with the advent of digital scanners, tissue histopathology slides can now be digitized and stored as digital images. As a result, digitized histopathological tissues can be used in computer-aided image analysis programs and machine learning techniques. Detection and segmentation of nuclei are some of the essential steps in the diagnosis of cancers. Recently, deep learning has been used for nuclei segmentation. However, one of the problems in deep learning methods for nuclei segmentation is the lack of information from out of the patches. This paper proposes a deep learning-based approach for nuclei segmentation, which addresses the problem of misprediction in patch border areas. We use both local and global patches to predict the final segmentation map. Experimental results on the Multi-organ histopathology dataset demonstrate that our method outperforms the baseline nuclei segmentation and popular segmentation models.

翻译：病理学是现代医学中最重要的发展之一。病理学检查是医学规程的金质标准,在诊断中起着根本作用。最近,随着数字扫描仪的出现,组织病理学幻灯片现在可以数字化并存储为数字图像。结果,数字化的病理学组织可以用于计算机辅助的图像分析程序和机器学习技术。核的检测和分解是诊断癌症的一些必要步骤。最近,在核分离方面使用了深层次的学习方法。然而,核分离的深层次学习方法的一个问题在于缺乏来自补丁的信息。本文建议对核分离采取深层次的学习方法,解决补丁边境地区的误入问题。我们用本地和全球的补丁来预测最后的分解图。多机核病理学数据集的实验结果表明,我们的方法超过了基线核分解和流行分解模型。