论文标题
核实例分割和组织病理学图像中的分类
Nuclei instance segmentation and classification in histopathology images with StarDist
论文作者
论文摘要
核的实例分割和分类是计算病理学中的重要任务。我们表明，最初用于荧光显微镜开发的深度学习核分割方法可以扩展并成功地应用于组织病理学图像。通过在蜥蜴数据集上进行实验，以及进入结肠核识别和计数（CONIC）挑战2022，我们的方法获得了排行榜上的第一个位置，以实现初步和最终测试阶段的分类和分类任务，从而证实了这一点。
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.
