Abstract
Accurate segmentation of cellular structures is critical for automating the analysis of microscopy data. Advances in deep learning have facilitated extensive improvements in semantic image segmentation. In particular, U-Net, a model specifically developed for biomedical image data, performs multi-instance segmentation through pixel-based classification. However, approaches based on U-Net tend to merge touching cells in dense cell cultures, resulting in under-segmentation. To address this issue, we propose DeepSplit; a multi-task convolutional neural network architecture where one encoding path splits into two decoding branches. DeepSplit first learns segmentation masks, then explicitly learns the more challenging cell-cell contact regions. We test our approach on a challenging dataset of cells that are highly variable in terms of shape and intensity. DeepSplit achieves 90% cell detection coefficient and 90% Dice Similarity Coefficient (DSC) which is a significant improvement on the state-of-the-art U-Net that scored 70% and 84% respectively.
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A Appendix
Branch-Net architecture. The orange blocks highlight the additional convolutional layers. (Color figure online)
Double-U-Net architecture. Orange highlights the additional U used for separation. (Color figure online)
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Torr, A., Basaran, D., Sero, J., Rittscher, J., Sailem, H. (2020). DeepSplit: Segmentation of Microscopy Images Using Multi-task Convolutional Networks. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_13
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