Accurate segmentation of nuclear instances using a double-stage neural network

Kesi Xu; Mostafa Jahanifar; Simon Graham; Nasir Rajpoot

doi:10.1117/12.2654173

6 April 2023 Accurate segmentation of nuclear instances using a double-stage neural network

Kesi Xu, Mostafa Jahanifar, Simon Graham, Nasir Rajpoot

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124711Y (2023) https://doi.org/10.1117/12.2654173
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Automatic nuclear instance segmentation is a crucial task in computational pathology as this information is required for extracting cell-based features in down-stream analysis. However, instance segmentation is a challenging task due to the nature of histology images which show large variations and irregularities in nuclei appearances and arrangements. Various deep learning-based methods have tried to tackle these challenges, however, most of these methods fail to segment the nuclei instances in crowded scenes accurately, or they are not fast enough for practical usage. In this paper, we propose a double-stage neural network for nuclear instance segmentation which leverages the power of an interactive model, NuClick, for accurate instance segmentation by replacing the user input with a nuclei detection module, YOLOv5. We optimized the proposed method to be lightweight and fast and show that it can achieve promising results when tested on the largest publicly available nuclei dataset.

Citation Download Citation

Kesi Xu, Mostafa Jahanifar, Simon Graham, and Nasir Rajpoot "Accurate segmentation of nuclear instances using a double-stage neural network", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124711Y (6 April 2023); https://doi.org/10.1117/12.2654173

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