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Authors: Sachin Bahade ; Michael Edwards and Xianghua Xie

Affiliation: Department of Computer Science, Swansea University, U.K.

Keyword(s): Graph Convolution Network, Signal Processing, Cell Segmentation, Medical Imaging.

Abstract: Graph signal processing is an emerging field in deep learning, aiming to solve various non-Euclidean domain problems. Pathologist have difficulty detecting diseases at an early stage due to the limitations of clinical methods and image analysis. For more accurate diagnosis of disease and early detection, automated segmentation can play a vital role. However, efficiency and accuracy of the system depends on how the model learned. We have found that traditional machine-learning methods, such as clustering and thresholding, are unsuited for precise cell segmentation. Furthermore, the recent development of deep-learning techniques has demonstrated promising results, especially for medical images. In this paper, we proposed two graph-based convolution methods for cell segmentation to improve analysis of immunostained slides. Our proposed methods use advanced deep-learning, spectral-, and spatial-based graph signal processing approaches to learn features. We have compared our results with state-of-the-art fully convolutional networks(FCN) method and found a significant of improvement of 2.2% in the spectral-based approach and 3.94% in the spatial-based approach in pixel based accuracy. (More)

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Paper citation in several formats:
Bahade, S.; Edwards, M. and Xie, X. (2021). Graph Convolution Networks for Cell Segmentation. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM, ISBN 978-989-758-486-2; ISSN 2184-4313, pages 620-627. DOI: 10.5220/0010324306200627

@conference{icpram21,
author={Sachin Bahade. and Michael Edwards. and Xianghua Xie.},
title={Graph Convolution Networks for Cell Segmentation},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM,},
year={2021},
pages={620-627},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010324306200627},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM,
TI - Graph Convolution Networks for Cell Segmentation
SN - 978-989-758-486-2
IS - 2184-4313
AU - Bahade, S.
AU - Edwards, M.
AU - Xie, X.
PY - 2021
SP - 620
EP - 627
DO - 10.5220/0010324306200627