Object segmentation is an important step in the workflow of computational
pathology. Deep learning based models generally require large amount of labeled
data for precise and reliable prediction. However, collecting labeled data is
expensive because it often requires expert knowledge, particularly in medical
imaging domain where labels are the result of a time-consuming analysis made by
one or more human experts. As nuclei, cells and glands are fundamental objects
for downstream analysis in computational pathology/cytology, in this paper we
propose a simple CNN-based approach to speed up collecting annotations for
these objects which requires minimum interaction from the annotator. We show
that for nuclei and cells in histology and cytology images, one click inside
each object is enough for NuClick to yield a precise annotation. For
multicellular structures such as glands, we propose a novel approach to provide
the NuClick with a squiggle as a guiding signal, enabling it to segment the
glandular boundaries. These supervisory signals are fed to the network as
auxiliary inputs along with RGB channels. With detailed experiments, we show
that NuClick is adaptable to the object scale, robust against variations in the
user input, adaptable to new domains, and delivers reliable annotations. An
instance segmentation model trained on masks generated by NuClick achieved the
first rank in LYON19 challenge. As exemplar outputs of our framework, we are
releasing two datasets: 1) a dataset of lymphocyte annotations within IHC
images, and 2) a dataset of segmented WBCs in blood smear images.

本论文提出了一种基于 CNN 的方法 NuClick，可以快速收集核、细胞，以及腺体细胞等许多应用于计算机病理学 / 细胞学的计算机处理对象的注释，并提出了一种新的引导信号，使 NuClick 能够分割腺体的边界。在实验证明，NuClick 能够适应不同的目标尺度，对用户输入的不同变化具有强大的鲁棒性，并且提供可靠的标注，对它标准产生的实例分割模型在 LYON19 挑战中获得了第一名。