深度學習計算成像系統於生物醫學顯微影像之重建與分析

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姓名

雷翔宇(Hsiang-Yu Lei) 查詢紙本館藏

畢業系所

生醫科學與工程學系

論文名稱

深度學習計算成像系統於生物醫學顯微影像之重建與分析
(Deep Learning-based Computational Imaging System for Reconstruction and Analysis of Biomedical Microscopy Image)

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摘要(中)

本研究使用深度學習神經網路方式建立影像重建與自動辨識功能，在基於編碼與解碼(Encoder-Decoder)框架下搭建一平行式深度學習神經網路模型，將所記錄的光學繞射訊號進行影像之重建，達到具傳統光學顯微鏡20倍物鏡下影像解析度，並透過遮罩區域卷積神經網路進行物件偵測，利用邊界框鎖定目標偵測物進行定位與搭配遮罩描繪出物體的輪廓實現例項分割，再加以對目標偵測物進行分類分析。本光學繞射訊號經由建立一套新型計算成像系統所獲取，具有顯微系統微型化與大幅提升影像視野之功能，此無透鏡光學影像還原系統與演算方法利用純量繞射理論簡化了光學成像設備，免除龐大複雜的光學元件，只需由非同調光與影像傳感器組成，透過控制光源的空間相干性在傳感器上記錄光學繞射訊號，重建之顯微影像解析度在0.6um狀態下，視場範圍可高達30mm2。
最終我們使用人體血液檢體與鼻咽癌細胞進行實際細胞影像還原與辨識以驗證本研究所研製之系統與重建模型的效果。透過細胞的表徵分析、分類、定位以及自動計數，證實本研究所建立具自動辨識分析之計算成像系統將有機會取代傳統顯微鏡進行更快速有效的影像辨識與分析。

摘要(英)

In this study, deep learning is used to reconstruct image and implement the object detection. A parallel deep learning neural network model, which is based on the Encoder-Decoder framework, is built to reconstruct the recorded diffraction patterns back to the images taken by a traditional microscope with a 20x objective lens. Then, Mask Region-based Convolutional Neural Network (Mask R-CNN) is used to not only implement the object detection for the reconstruction images through bounding boxes and achieve instance segmentation by generating mask but also classify each object. The diffraction patterns are recorded by a new computational imaging system which miniaturizes the microscopy system and greatly expands the field of view. This lens-free computational imaging system and calculation method simplify the optical imaging equipment through scalar diffraction theory. It eliminates the need for large and complex optical components and is only composed of light source and image sensor. By controlling the spatial coherence of the light source, the diffraction patterns are recorded at 0.6um resolution, and the field of view can be up to 30mm2.
In the end, we take human blood and Nasopharyngeal Carcinoma cancer cells as samples of reconstruction and object detection to verify the effect of the system and reconstruction model built in this study. Through implementing of cell characterization analysis, classification, detection and automatic counting, the computational imaging system of this study can be supposed to have the opportunity to replace traditional microscopes by fast and effective image identification and analysis.

關鍵字(中)

★ 深度學習
★ 物件偵測
★ 例項分割
★ 計算成像系統
★ 血液
★ 鼻咽癌細胞

關鍵字(英)

★ deep learning
★ object detection
★ instance segmentation
★ computational imaging system
★ human blood
★ Nasopharyngeal Carcinoma cancer cell

論文目次

中文摘要.....i
ABSTRACT.....ii
致謝.....iii
目錄.....iv
圖目錄.....vii
表目錄.....ix
第一章緒論.....1
1-1 研究動機與目的.....1
1-2 論文架構.....1
第二章文獻探討.....2
2-1 人工智慧.....2
2-2 人工神經網路.....2
2-2-1 單層感知器(Single-layer Perceptron).....3
2-2-2 多層感知器(Multilayer Perceptron).....4
2-2-3 卷積神經網路.....4
2-3 全像術.....7
2-3-1 全像術理論.....7
2-3-2 數位全像術.....8
2-4 血液細胞形態學與全血細胞計數.....9
第三章研究方法.....11
3-1 研究方法與架構.....11
3-2 樣本製備.....12
3-2-1 血液樣本製備.....12
3-2-2 鼻咽癌細胞培養.....13
3-3 系統架設.....14
3-4 資料預處理.....16
3-4-1 基於灰度世界演算法之白平衡處理.....16
3-4-2 亮度對比度調整.....19
3-4-3 資料擴增.....20
3-5 影像重建之深度學習模型.....23
3-5-1 影像重建之深度學習模型架構.....23
3-5-2 編碼與解碼(Encoder-decoder)之應用.....23
3-5-3平行式網路模型演算結構.....24
3-6 血球細胞辨識及自動計數分析系統.....27
3-6-1 系統目的及流程.....27
3-6-2 基於Mask R-CNN模型之演算架構.....28
3-6-3 Mask R-CNN之Backbone.....30
3-6-4 Mask R-CNN之RoIAlign.....38
3-6-5 Mask R-CNN之Head.....41
3-6-6 Mask R-CNN之Loss Function.....43
3-7 深度學習模型軟硬體設備.....45
第四章研究結果分析與討論.....46
4-1 影像重建結果分析.....46
4-1-1 血液細胞影像與鼻咽癌細胞影像資料集.....46
4-1-2 結構相似性指標.....48
4-1-3 基於全卷積神經網路之影像重建.....50
4-1-4 白平衡預處理之效果.....53
4-2 血液細胞影像分析與討論.....53
4-2-1 血液細胞影像資料集與標記.....53
4-2-2 血球細胞定位以及系統自動計數之分析.....57
第五章結論與未來展望.....59
5-1 結論.....59
5-2 未來展望.....60
參考文獻 Reference.....61

參考文獻

參考文獻 Reference
[1] Zhu, Hongying, et al. "Optical imaging techniques for point-of-care diagnostics." Lab on a Chip 13.1 (2013): 51-67.
[2] McCarthy, John, et al. "A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955." AI magazine 27.4 (2006): 12-12
[3] Flowers, Johnathan Charles. "Strong and Weak AI: Deweyan Considerations." AAAI Spring Symposium: Towards Conscious AI Systems. 2019.
[4] T. M. Mitchell, "Machine learning. 1997," Burr Ridge, IL: McGraw Hill, vol. 45, no. 37, pp. 870-877, 1997.
[5] Michie, Donald, David J. Spiegelhalter, and Charles C. Taylor. "Machine learning, neural and statistical classification." (1994).
[6] Cortes, Corinna, and Vladimir Vapnik. "Support-vector networks." Machine learning 20.3 (1995): 273-297.
[7] Dey, Ayon. "Machine learning algorithms: a review." International Journal of Computer Science and Information Technologies 7.3 (2016): 1174-1179.
[8] LeCun, Yann, Yoshua Bengio, Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.
[9] Dreiseitl, Stephan, and Lucila Ohno-Machado. "Logistic regression and artificial neural network classification models: a methodology review." Journal of biomedical informatics 35.5-6 (2002): 352-359.
[10] LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998): 2278-2324.
[11] Bian, Yinxu, et al. "Optical refractometry using lensless holography and autofocusing." Optics express 26.23 (2018): 29614-29628.

[12] Seo, Sungkyu, et al. "Lensfree holographic imaging for on-chip cytometry and diagnostics." Lab on a Chip 9.6 (2009): 777-787.
[13] Garcia-Sucerquia, Jorge, et al. "Digital in-line holographic microscopy." Applied optics 45.5 (2006): 836-850.
[14] Rivenson, Yair, et al. "Phase recovery and holographic image reconstruction using deep learning in neural networks." Light: Science & Applications 7.2 (2018): 17141-17141.
[15] Liao, Shuen-Kuei, et al. "Chromosomal abnormalities of a new nasopharyngeal carcinoma cell line (NPC-BM1) derived from a bone marrow metastatic lesion." Cancer genetics and cytogenetics 103.1 (1998): 52-58.
[16] Asghar, Waseem, et al. "Engineering long shelf life multi-layer biologically active surfaces on microfluidic devices for point of care applications." Scientific reports 6.1 (2016): 1-10.
[17] Sobieranski, Antonio C., et al. "Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution." Light: Science & Applications 4.10 (2015): e346-e346.
[18] Tseng, Derek, et al. "Lensfree microscopy on a cellphone." Lab on a Chip 10.14 (2010): 1787-1792.
[19] Kwok, Ngai M., et al. "Gray world based color correction and intensity preservation for image enhancement." 2011 4th International Congress on Image and Signal Processing. Vol. 2. IEEE, 2011.
[20] Kim, Seung-Wook, et al. "Parallel feature pyramid network for object detection." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
[21] Sun, Ke, et al. "High-resolution representations for labeling pixels and regions." arXiv preprint arXiv:1904.04514 (2019).
[22] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[23] Dumoulin, Vincent, and Francesco Visin. "A guide to convolution arithmetic for deep learning." arXiv preprint arXiv:1603.07285 (2016).
[24] He, Kaiming, et al. "Mask r-cnn." Proceedings of the IEEE international conference on computer vision. 2017.
[25] Girshick, Ross, et al. "Region-based convolutional networks for accurate object detection and segmentation." IEEE transactions on pattern analysis and machine intelligence 38.1 (2015): 142-158.
[26] Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE international conference on computer vision. 2015.
[27] Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks." arXiv preprint arXiv:1506.01497 (2015).
[28] Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
[29] Uijlings, Jasper RR, et al. "Selective search for object recognition." International journal of computer vision 104.2 (2013): 154-171.
[30] Wang, Zhou, et al. "Image quality assessment: from error visibility to structural similarity." IEEE transactions on image processing 13.4 (2004): 600-612.
[31] Ndajah, Peter, et al. "SSIM image quality metric for denoised images." Proc. 3rd WSEAS Int. Conf. on Visualization, Imaging and Simulation. 2010.

指導教授

黃貞翰(Chen-Han Huang)

審核日期

2021-8-9

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