Abstract
Zebrafish is a popular model system for biomedical analysis, especially for compound screening in drug research. In this paper, we present a comprehensive investigation aimed at enhancing the processing pipeline for segmenting zebrafish larvae images. The emphasis is on the application of an unsupervised segmentation method for segmenting zebrafish in Optical Projection Tomography (OPT) images. We propose a novel pipeline that integrates the Transformer and U-Net, a convolutional neural network for biomedical image segmentation, to achieve accurate segmentation of zebrafish larvae images. This accuracy is critical for precise 3D reconstruction. Leveraging transfer learning, we broaden the capabilities of our trained model to segment OPT images.
This approach is intended to enhance the robustness and versatility of our pipeline, allowing it to cater to a broad range of imaging modalities beyond traditional microscopic images. The developed processing pipeline is then used for 3D reconstruction of the segmented areas, demonstrating its potential for advanced biomedical analysis. Our findings confirm the efficiency and accuracy of the proposed pipeline providing robust tools for future Zebrafish-based research, particularly in the domains of drug screening and cancer treatment.
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Javanmardi, S., Tang, X., Jahanbanifard, M., Verbeek, F.J. (2023). Unsupervised Segmentation of High-Throughput Zebrafish Images Using Deep Neural Networks and Transformers. In: Anutariya, C., Bonsangue, M.M. (eds) Data Science and Artificial Intelligence. DSAI 2023. Communications in Computer and Information Science, vol 1942. Springer, Singapore. https://doi.org/10.1007/978-981-99-7969-1_16
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DOI: https://doi.org/10.1007/978-981-99-7969-1_16
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