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NuClick : a deep learning framework for interactive segmentation of microscopic images
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Koohbanani, Navid Alemi, Jahanifar, Mostafa, Tajadin, Neda Zamani and Rajpoot, Nasir M. (Nasir Mahmood) (2020) NuClick : a deep learning framework for interactive segmentation of microscopic images. Medical Image Analysis, 65 . 101771. doi:10.1016/j.media.2020.101771 ISSN 1361-8415.
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Official URL: http://dx.doi.org/10.1016/j.media.2020.101771
Abstract
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 NuClick, a CNN-based approach to speed up collecting annotations for these objects requiring 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 applicable to a wide range of object scales, 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.
Item Type: | Journal Article | ||||||||||||
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Subjects: | Q Science > Q Science (General) R Medicine > RB Pathology R Medicine > RC Internal medicine T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
Library of Congress Subject Headings (LCSH): | Image processing -- Digital techniques, Image analysis, Pathology -- Data processing, Machine learning | ||||||||||||
Journal or Publication Title: | Medical Image Analysis | ||||||||||||
Publisher: | Elsevier | ||||||||||||
ISSN: | 1361-8415 | ||||||||||||
Official Date: | October 2020 | ||||||||||||
Dates: |
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Volume: | 65 | ||||||||||||
Article Number: | 101771 | ||||||||||||
DOI: | 10.1016/j.media.2020.101771 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
Date of first compliant deposit: | 15 July 2020 | ||||||||||||
Date of first compliant Open Access: | 10 July 2021 | ||||||||||||
RIOXX Funder/Project Grant: |
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