Instance segmentation and classification of nuclei is an important task in computational pathology. We show that StarDist, a deep learning nuclei segmentation method originally developed for fluorescence microscopy, can be extended and successfully applied to histopathology images. This is substantiated by conducting experiments on the Lizard dataset, and through entering the Colon Nuclei Identification and Counting (CoNIC) challenge 2022, where our approach achieved the first spot on the leaderboard for the segmentation and classification task for both the preliminary and final test phase.

翻译：在计算病理学中,核的发生分解和分类是一项重要任务。我们显示,星磁是最初为荧光显微镜而开发的一种深学习的核分解方法,可以推广并成功地应用于组织病理学图象,通过对蜥蜴数据集的实验和进入Colon Nuclei识别和计数(CoNIC)挑战2022,我们的方法在初步和最后试验阶段的分解和分类任务上都达到了首选位置。