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系統識別號 U0026-2207202014100400
論文名稱(中文) 應用於細菌的抗生素藥敏試驗之深度學習顯微影像系統研究
論文名稱(英文) Deep Learning-based Microscopy Imaging System for Antimicrobial Susceptibility Test
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 林均澤
研究生(英文) Chin-Tse Lin
學號 P86071121
學位類別 碩士
語文別 英文
論文頁數 52頁
口試委員 口試委員-孫永年
口試委員-柯文謙
口試委員-陳柏齡
口試委員-黃建璋
指導教授-張憲彰
中文關鍵字 抗生素藥敏試驗  深度學習  機器學習  細菌檢測  顯微影像 
英文關鍵字 Antimicrobial susceptibility testing (AST)  Deep learning  Machine learning  Bacteria detection  Microscopy imaging 
學科別分類
中文摘要 當前的抗生素藥敏試驗通常需要三、四天的檢測時間,並且仰賴大量的人工操作,冗長的檢測時間將會延遲細菌感染在早期的治療決策,嚴重的細菌感染很可能導致敗血症,而敗血症的死亡率,當治療時間每延遲一小時,死亡率便會增加7.6%。因此現今迫切地需要一種更快速的抗生素藥敏試驗解決方法,來提供醫生能更有效地開定精確的抗生素處方。可從先前的研究發現,細菌在抗生素的作用下會產生型態的變化,從而可被應用於判定藥敏試驗的結果。本研究根據此特性提出了一種應用於革蘭氏陰性細菌的抗生素藥物耐受性試驗之深度學習顯微影像系統,我們利用深度學習的演算法,辨識出在抗生素作用下不同細菌型態的變化,並使用機器學習的方法進行迴歸分析,檢討是否可測定出在不同濃度抗生素作用下之針對該細菌的最小抑菌濃度 (MIC)。此系統實際上利用標準菌株大腸桿菌 (ATCC 25922),在頭孢唑林、頭孢他啶、頭孢吡肟等三種不同代數之頭孢菌素的抗生素濃度組合下進行初步驗證,我們成功於2小時的培養後取得且判讀出不同抗生素濃度的影像,進而判定得知其MIC值,準確度均可以達到95%。本研究將顯微影像系統與軟體整合,與傳統的方法比較,通過簡單的手動操作程序,將試驗整體的時間從3-4天,縮短為4-5小時,並減少了人力成本。
英文摘要 The current antimicrobial susceptibility testing (AST) method usually takes a few days and labor-intensive, which will delay initial treatment decisions in the early stages of bacterial infections. The severe bacterial infection is very likely to cause sepsis, and the mortality rate of sepsis will increase by 7.6% per hour when effective treatments are delayed. It shows that there is an urgent need for a rapid solution to help doctors prescribe antibiotics more effectively. Previous research shows that that the morphology of bacteria will change under beta-lactam antibiotics treatment. From the characteristic of bacteria, we propose a rapid AST system for Gram-negative bacteria, using deep learning algorithms to identify different bacterial morphology changes. Moreover, using a machine learning method to do the classification automatically determines the minimum inhibitory concentration (MIC) bacteria at different concentration of antibiotics. The system uses the Escherichia coli (ATCC 25922) to conduct preliminary study of cefazolin, ceftazidime and cefepime treatments. The MIC can be determined after 2 hours. The accuracy can reach 95%. Our system integrates the microscopic imaging system and software, which can reduce the detection time from three to four days to 4-5 hours with simple manual operations procedures.
論文目次 Abstract I
中文摘要 II
致謝 III
Contents IV
List of Figures VII
List of Tables IX
Chapter 1 Introduction 1
1.1 Background 1
1.2 Antimicrobial susceptibility testing 2
1.2.1 Minimal inhibitory concentration (MIC) 2
1.2.2 Traditional AST methods 3
1.2.3 Current AST methods 5
1.2.4 Review of AST development 6
1.3 Mechanism of antibiotics 7
1.3.1 Types and actions of different antibiotics 7
1.3.2 Evolution of Cephalosporin 9
1.3.3 Bacterial morphology under antibiotics 10
1.4 Introduction of deep learning 11
1.4.1 Novel deep learning models 11
1.4.2 Deep learning in the medical field 12
1.4.3 Image processing method for bacteria image 13
1.5 Motivation and Aims 14
Chapter 2 Materials and Methods 16
2.1 Image system design 16
2.1.1 Image parameters 16
2.1.2 Introduction of the image system 17
2.1.3 User interface of the image capture 18
2.1.4 Data collection 19
2.2 AST interpretation 20
2.2.1 Bacteria culture 20
2.2.2 Standard bacteria and their MIC 20
2.3 Culture wells 21
2.3.1 Volume test 21
2.3.2 Chip structure 22
2.4 Data preprocessing 23
2.4.1 Image preprocessing 23
2.4.2 Image labeling 23
2.5 Convolutional neural network 24
2.5.1 Convolution layer 24
2.5.2 Pooling layer 25
2.6 Object detection 26
2.7 YOLO (You only look once) 27
2.7.1 Development of YOLO 27
2.7.2 Introduction of YOLOv3 29
2.7.3 Structure of YOLOv3 30
Chapter 3 Results and Discussion 31
3.1 Evaluating object detection models 31
3.1.1 Intersection over union (IoU) 31
3.1.2 Mean average precision (mAP) 32
3.1.3 K-fold cross-validation 34
3.1.4 Performance of different input sizes 37
3.1.5 Data augmentation 38
3.2 Results of AST report 40
3.2.1 Statistics of bounding box 40
3.2.2 Logistic regression 44
3.2.3 AST results from ML model 45
3.2.4 Results from different β-lactam antibiotic 46
Chapter 4 Conclusion and Prospects 48
Reference 49
Personal Information 52
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