High-content image analysis of patient-derived organoids:
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Heidelberg
2018
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | 106 Seiten Illustrationen, Diagramme 30 cm |
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MARC
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| 100 | 1 | |a Sauer, Jan |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a High-content image analysis of patient-derived organoids |c presented by Jan Sauer, MSc. |
| 264 | 1 | |a Heidelberg |c 2018 | |
| 300 | |a 106 Seiten |b Illustrationen, Diagramme |c 30 cm | ||
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| 502 | |b Dissertation |c Ruperto Carola University Heidelberg, Germany |d 2018 | ||
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Record in the Search Index
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| adam_text | C O N TEN TS
1 INTRODUCTION 1
1.1 PRECISION MEDICINE AND THE GENETIC BASIS OF DISEASES
........................
1
1.2 PATIENT-SPECIFIC DRUG
THERAPY............................................................... 2
1.2.1 MEASURING DRUG
RESPONSE......................................................... 3
1.2.2 IN VITRO CANCER M O D E LS
............................................................ 4
1.3 IMAGE ANALYSIS OF MICROSCOPY
SCREENS................................................ 6
1.3.1 PROJECTING 3D IMAGE STACKS
................................................... 6
1.3.2 SEGMENTING MICROSCOPY
IMAGES................................................ 7
1.3.3 FEATURE EXTRACTION
.................................................................. 9
1.3.4 DOWNSTREAM ANALYSIS OF D A TA
.......................................................10
1.3.5 BATCH EFFECT CORRECTION
............................................................
11
1.4 DEEP LE A RN IN G
..........................................................................................
11
1.4.1 INTRODUCTION TO DEEP NEURAL NETW ORKS
.......................................
12
1.4.2 DEEP NEURAL NETWORK ARCHITECTURES
.............................................
15
1.4.3 DEEP NEURAL NETWORKS IN BIO LO GY
................................................
16
1.5 ABOUT THIS TH E S IS
....................................................................................
18
1.5.1 AIM AND OU TLINE
...........................................................................
18
1.5.2 ABOUT THE D A T A
...........................................................................
19
1.5.3 MANUSCRIPT DERIVED FROM THIS TH E S IS
..........................................
22
2 HIGH-THROUGHPUT IMAGE ANALYSIS PIPELINE 23
2.1
RESULTS......................................................................................................
23
2.1.1 MAXIMUM CONTRAST PROJECTION OF IMAGE S TA C K S
..........................
23
2.1.2 SEGMENTATION WITH A SEMI-SUPERVISED DEEP NEURAL NETWORK . 25
2.1.3 ORGANOID QUALITY CO N TRO L
..............................
30
2.2
DISCUSSION................................................................................................31
2.2.1 MAXIMIZING THE INFORMATION DENSITY WITH A MAXIMUM CON
TRAST P RO JE CTIO N
...........................................................................
32
2.2.2 SEGMENTING IMAGES WITH A NOISE-ROBUST DEEP NEURAL NETWORK 33
2.2.3 ORGANOID QUALITY C O N TRO
L.............................................................34
2.2.4 ANALYSIS TIME IS THROTTLED BY DATA TRA N S FE R
..............................
35
2.3 M E TH O D
S...................................................................................................
36
2.3.1 IMPLEMENTATION OF
PIPELINE..........................................................36
2.3.2 DEFINITION OF THE MAXIMUM CONTRAST
PROJECTION.........................36
2.3.3 SEMI-SUPERVISED SEGMENTATION A LG O RITH M
..................................37
2.3.4 FEATURE EXTRACTION
......................................................................39
2.3.5 BATCH EFFECT CO RRE C TIO N
................................................................39
3 DRUG-INDUCED PHENOTYPE ANALYSIS 41
3.1
RESULTS.......................................................................................................41
3.1.1 EXPLORATORY ANALYSIS AND ORGANOID
HETEROGENEITY......................41
3.1.2 DRUG-INDUCED ORGANOID V IA B ILITY
.................................................42
3.1.3 UNSUPERVISED EXPLORATION OF GENERAL DRUG-INDUCED PHENOTYPES 54
3.2
DISCUSSION.................................................................................................62
3.2.1 HIGH-CONTENT IMAGE SCREENING FOR THERAPY RECOMMENDATION . 62
3.2.2 DRUG-INDUCED PHENOTYPES REFLECT GENETIC PERTURBATIONS . . . 64
3.2.3 BATCH EFFECTS ARE NOT LIMITED TO BASE
PHENOTYPES......................66
3.2.4 WELL-AVERAGED ORGANOID FEATURES DO NOT REFLECT SCREEN QUALITY 66
3.3 M E TH O D
S....................................................................................................67
3.3.1 HIERARCHICAL CLUSTERING P RO C E D U RE
..............................................67
3.3.2 RANDOM FOREST CLASSIFIER FOR DETERMINING ORGANOID VIABILITY . 67
3.3.3 DRUG-INDUCED PHENOTYPE DEFINITION AND C LU S TE RIN G
...................68
3.3.4 EXACT FISHER TEST FOR HIERARCHICAL
CLUSTERING...............................69
3.3.5 PERMUTATION TEST FOR SUPPORT VECTOR M A CH IN E
S.........................69
4 TRANSFER LEARNING: IMAGE ANALYSIS WITHOUT SEGMENTATION 70
4.1 IN TRO D U CTIO N
..............................................................................................70
4.2
RESULTS.......................................................................................................72
4.3
DISCUSSION.................................................................................................75
4.4 M E THOD
S....................................................................................................77
4.4.1 FEATURE EXTRACTION VIA TRANSFER
LEARNING.....................................77
4.4.2 PREPROCESSING PIPELINE FOR TRANSFER LEARNING FE A TU RE
S................77
4.4.3 DRUG-INDUCED PHENOTYPE D E FIN ITIO N
...........................................77
5 CONCLUSION AND OUTLOOK 78
A APPENDIX 81
A .L LIST OF
FIGURES..........................................................................................81
A.2 LIST OF T A B LE S
..........................................................................................
82
B BIBLIOGRAPHY
95
|
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| illustrated | Illustrated |
| indexdate | 2024-07-10T08:33:26Z |
| institution | BVB |
| language | English |
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| physical | 106 Seiten Illustrationen, Diagramme 30 cm |
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| spelling | Sauer, Jan Verfasser aut High-content image analysis of patient-derived organoids presented by Jan Sauer, MSc. Heidelberg 2018 106 Seiten Illustrationen, Diagramme 30 cm txt rdacontent n rdamedia nc rdacarrier Dissertation Ruperto Carola University Heidelberg, Germany 2018 (DE-588)4113937-9 Hochschulschrift gnd-content DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031418346&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sauer, Jan High-content image analysis of patient-derived organoids |
| subject_GND | (DE-588)4113937-9 |
| title | High-content image analysis of patient-derived organoids |
| title_auth | High-content image analysis of patient-derived organoids |
| title_exact_search | High-content image analysis of patient-derived organoids |
| title_full | High-content image analysis of patient-derived organoids presented by Jan Sauer, MSc. |
| title_fullStr | High-content image analysis of patient-derived organoids presented by Jan Sauer, MSc. |
| title_full_unstemmed | High-content image analysis of patient-derived organoids presented by Jan Sauer, MSc. |
| title_short | High-content image analysis of patient-derived organoids |
| title_sort | high content image analysis of patient derived organoids |
| topic_facet | Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031418346&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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