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研究生: 戚瀚文
Chi, Han-Wen
論文名稱: 深度學習輔助全像斷層三維影像分割及資料視覺化
Deep learning–assisted three-dimensional segmentation for data visualization of holographic tomography
指導教授: 鄭超仁
Cheng, Chau-Jern
杜翰艷
Tu, Han-Yen
口試委員: 林立謙
Lin, Li-Chien
林昱志
Lin, Yu-Chih
杜翰艷
Tu, Han-Yen
鄭超仁
Cheng, Chau-Jern
口試日期: 2023/10/27
學位類別: 碩士
Master
系所名稱: 光電工程研究所
Graduate Institute of Electro-Optical Engineering
論文出版年: 2023
畢業學年度: 112
語文別: 中文
論文頁數: 58
中文關鍵詞: 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
英文關鍵詞: holographic tomography, three-dimensional cell images segmentation, deep learning, RGB holographic display, data visualization
研究方法: 實驗設計法
DOI URL: http://doi.org/10.6345/NTNU202301828
論文種類: 學術論文
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  • 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.

    第一章 緒論1 1.1 數位全像造影技術發展與應用1 1.2 文獻回顧4 1.2.1 全像斷層4 1.2.2 深度學習用於生物細胞分割5 1.2.3 三維全像顯示6 1.3 研究目的與動機8 1.4 論文架構9 第二章 數位全像與全像斷層原理10 2.1 數位全像記錄與重建10 2.2 全像斷層記錄與重建11 第三章 深度學習輔助全像斷層三維影像分割方法14 3.1 深度學習簡介14 3.1.1 深度學習原理14 3.1.2 3D U-net20 3.2 基於全像斷層之深度學習細胞分割21 3.2.1 全像斷層系統架構21 3.2.2 細胞型態變化22 3.2.3 細胞體/細胞核分割方法23 3.2.4 深度學習模型建置26 3.3 深度學習分割結果量化分析28 3.3.1 電腦系統規格與實驗環境28 3.3.2 效能評估29 第四章 全像顯示細胞三維模型 4.1 電腦全像術31 4.1.1 電腦全像術原理31 4.1.2 多平面Gerchberg–Saxton演算法35 4.2 全像顯示光學系統39 4.2.1 系統架構39 4.2.2 系統校正42 4.3 細胞三維顯示44 4.3.1 細胞三維模型之電腦全像片設計44 4.3.2 實驗結果46 第五章 結論與未來展望48 參考文獻49 附錄 發表論文(OPTIC2022) (FIO-LS 2023)54

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