Segmenting filamentous structures in confocal microscopy images is important for analyzing and quantifying related biological processes. However, thin structures, especially in noisy imagery, are difficult to accurately segment. In this paper, we introduce a novel deep network architecture for filament segmentation in confocal microscopy images that improves upon the state-of-the-art U-net and SOAX methods. We also propose a strategy for data annotation, and create datasets for microtubule and actin filaments. Our experiments show that our proposed network outperforms state-of-the-art approaches and that our segmentation results are not only better in terms of accuracy, but also more suitable for biological analysis and understanding by reducing the number of falsely disconnected filaments in segmentation.
CITATION STYLE
Liu, Y., Treible, W., Kolagunda, A., Nedo, A., Saponaro, P., Caplan, J., & Kambhamettu, C. (2019). Densely connected stacked u-network for filament segmentation in microscopy images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11134 LNCS, pp. 403–411). Springer Verlag. https://doi.org/10.1007/978-3-030-11024-6_30
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