论文标题
结肠核识别和计数的同时语义和实例分割
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting
论文作者
论文摘要
我们解决了来自降血石和曙红染色的组织学图像的自动分割，分类和定量的问题，这与几种下游计算病理学应用非常相关。在这项工作中，我们提出了一个解决方案，作为同时的语义和实例分割框架。我们的解决方案是结肠核鉴定和计数（圆锥）挑战的一部分。我们首先分别训练语义和实例分割模型。我们的框架用作骨干悬停网和级联面膜RCNN型号。然后，我们将结果与自定义的非最大抑制嵌入（NMS）结合。在我们的框架中，语义模型在实例模型提供精制分割时计算单元格的类预测。通过我们的实验结果，我们证明了我们的模型的表现要优于提供的基准。
We address the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for several downstream computational pathology applications. In this work, we present a solution framed as a simultaneous semantic and instance segmentation framework. Our solution is part of the Colon Nuclei Identification and Counting (CoNIC) Challenge. We first train a semantic and instance segmentation model separately. Our framework uses as backbone HoverNet and Cascade Mask-RCNN models. We then ensemble the results with a custom Non-Maximum Suppression embedding (NMS). In our framework, the semantic model computes a class prediction for the cells whilst the instance model provides a refined segmentation. We demonstrate, through our experimental results, that our model outperforms the provided baselines by a large margin.
