Channel-wise Aggregation with Self-correction Mechanism for Multi-center Multi-Organ Nuclei Segmentation in Whole Slide Imaging

Mohamed Abdel-Nasser, Mohamed Abdel-Nasser, Adel Saleh, Domenec Puig

2020

Abstract

In the field of computational pathology, there is an essential need for accurate nuclei segmentation methods for performing different studies, such as cancer grading and cancer subtype classification. The ambiguous boundary between different cell nuclei and the other objects that have a similar appearance beside the overlapping and clumped nuclei may yield noise in the ground truth masks. To improve the segmentation results of cell nuclei in histopathological images, in this paper, we propose a new technique for aggregating the channel maps of semantic segmentation models. This technique is integrated with a self-correction learning mechanism that can handle noisy ground truth. We show that the proposed nuclei segmentation method gives promising results with images of different organs (e.g., breast, bladder, and colon)collected from medical centers that use devices of different manufacturers and stains. Our method reaches the new state-of-the-art. Mainly, we achieve the AJI score of 0.735 on the Multi-Organ Nuclei Segmentation benchmark, which outperforms the previous closest approaches.

Paper Citation

in Harvard Style

Abdel-Nasser M., Saleh A. and Puig D. (2020). Channel-wise Aggregation with Self-correction Mechanism for Multi-center Multi-Organ Nuclei Segmentation in Whole Slide Imaging. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 466-473. DOI: 10.5220/0009156604660473

in Bibtex Style

@conference{visapp20,
author={Mohamed Abdel-Nasser and Adel Saleh and Domenec Puig},
title={Channel-wise Aggregation with Self-correction Mechanism for Multi-center Multi-Organ Nuclei Segmentation in Whole Slide Imaging},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={466-473},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009156604660473},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Channel-wise Aggregation with Self-correction Mechanism for Multi-center Multi-Organ Nuclei Segmentation in Whole Slide Imaging
SN - 978-989-758-402-2
AU - Abdel-Nasser M.
AU - Saleh A.
AU - Puig D.
PY - 2020
SP - 466
EP - 473
DO - 10.5220/0009156604660473
PB - SciTePress