鄭超仁杜翰艷Cheng, Chau-JernTu, Han-Yen戚瀚文Chi, Han-Wen2024-12-179999-12-312023https://etds.lib.ntnu.edu.tw/thesis/detail/8bcfe6baefbdd990f71328a2db1eec96/http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/123156本研究主要探討如何將全像斷層造影系統所擷取的三維細胞影像進行分割，得到不同的細胞胞器三維模型，並且使用深度學習來輔助快速且自動化處理。此外，本研究將會進一步把分割好的影像編寫成電腦全像片，並會詳細說明設計三維電腦全像片演算法的原理以及實現方法，最後，將運用RGB全像顯示技術，以進行光學重建實現資料視覺化的呈現。This research primarily explores how to do the holographic tomography systems to captured three-dimensional cell images for segmentation to obtain distinct 3D models of cell organelles. It utilizes deep learning-assisted for fast and automated processing. Additionally, this research will further convert the segmented images into computer-generated holograms. The principles and implementation methods of the 3D computer-generated hologram algorithm will be elaborated upon. Finally, the RGB holographic display technique will be employee for optical reconstruction to achieve data visualization presentation.全像斷層三維細胞影像分割深度學習RGB全像顯示資料視覺化holographic tomographythree-dimensional cell images segmentationdeep learningRGB holographic displaydata visualization學術論文