Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation
Sensors 2022
Vilen Jumutc, Dmitrijs Bļizņuks, Alexey Lihachev

U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabilities, while addressing several immanent shortcomings, such as constrained resolution and non-resilient receptive fields of the main pathway. Our novel multi-path architecture introduces a notion of an individual receptive field pathway, which is merged with other pathways at the bottom-most layer by concatenation and subsequent application of Layer Normalization and Spatial Dropout, which can improve generalization performance for small datasets. In general, our experiments show that the proposed multi-path architecture outperforms other state-of-the-art approaches that embark on similar ideas of pyramid structures, skip-connections, and encoder–decoder pathways. A significant improvement of the Dice similarity coefficient is attained at our proprietary colony-forming unit dataset, where a score of 0.809 was achieved for the foreground class.


Keywords
U-Net; skip-connections; neural network; encoder–decoder; Layer Normalization
DOI
10.3390/s22030990
Hyperlink
https://www.mdpi.com/1424-8220/22/3/990

Jumutc, V., Bļizņuks, D., Lihachev, A. Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation. Sensors, 2022, Vol. 22, No. 3, Article number 990. ISSN 1424-8220. Available from: doi:10.3390/s22030990

Publication language
English (en)
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