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 timeconsuming 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. 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

Citation

@article{KOOHBANANI2020101771,
title = "NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopic Images",
journal = "Medical Image Analysis",
pages = "101771",
year = "2020",
issn = "1361-8415",
doi = "https://doi.org/10.1016/j.media.2020.101771",
url = "http://www.sciencedirect.com/science/article/pii/S1361841520301353",
author = "Navid Alemi Koohbanani and Mostafa Jahanifar and Neda Zamani Tajadin and Nasir Rajpoot"}

Dataset:

1 - Images of white blood cells (WBC) in blood sample images with their segmentation masks (download)

2 - Datasets of lymphocyte segmentation in Immunohistochemistry (IHC) images (download)

• Folder "IHC_xml_asap" consists of images in Pyramid TIF format and segmentations in XML format that can be opened in ASAP

If you intend to publish a research work that uses any of these datasets, you must cite the above publication.