The goal of this bachelor thesis is to develop and optimize a deep learning model for segmentation and analysis of cilia and flagella in multichannel microscopic images. Key objectives include automated cilia numbering, measuring their length and intensity across all channels, followed by additional analysis and post-processing. This includes, but is not limited to, utilizing metadata for processing information about imaging conditions. Additionally, the project focuses on determining intensity distribution within cilia to identify their starting points, handling overlapping structures, and improving segmentation accuracy for more reliable analysis.
References
Satir P, Christensen ST. Overview of structure and function of mammalian cilia. Annu Rev Physiol. 2007;69:377-400. doi: 10.1146/annurev.physiol.69.040705.141236. PMID: 17009929.
Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham.
