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Robust interactive semantic segmentation of pathology images with minimal user input
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Jahanifar, Mostafa, Tajeddin, Neda Zamani, Koohbanani, Navid Alemi and Rajpoot, Nasir M. (Nasir Mahmood) (2021) Robust interactive semantic segmentation of pathology images with minimal user input. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada, 11-17 Oct 2021. Published in: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) pp. 674-683. doi:10.1109/ICCVW54120.2021.00081
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Official URL: http://dx.doi.org/10.1109/ICCVW54120.2021.00081
Abstract
From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue regions in histology images is a prerequisite. However, delineating different tissue regions manually is a laborious, time-consuming and costly task that requires expert knowledge. On the other hand, the state-of-the-art automatic deep learning models for semantic segmentation require lots of annotated training data and there are only a limited number of tissue region annotated images publicly available. To obviate this issue in computational pathology projects and collect large-scale region annotations efficiently, we propose an efficient interactive segmentation network that requires minimum input from the user to accurately annotate different tissue types in the histology image. The user is only required to draw a simple squiggle inside each region of interest so it will be used as the guiding signal for the model. To deal with the complex appearance and amorph geometry of different tissue regions we introduce several automatic and minimalistic guiding signal generation techniques that help the model to become robust against the variation in the user input. By experimenting on a dataset of breast cancer images, we show that not only does our proposed method speed up the interactive annotation process, it can also outperform the existing automatic and interactive region segmentation models.
Item Type: | Conference Item (Paper) | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
Journal or Publication Title: | 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) | ||||
Publisher: | IEEE | ||||
Book Title: | 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) | ||||
Official Date: | 2021 | ||||
Dates: |
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Page Range: | pp. 674-683 | ||||
DOI: | 10.1109/ICCVW54120.2021.00081 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) | ||||
Type of Event: | Conference | ||||
Location of Event: | Montreal, BC, Canada | ||||
Date(s) of Event: | 11-17 Oct 2021 |
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