1. Research Direction
楊博凱

2. Problem
How to optimally select the embryo with the highest potential for pregnancy? ?

3. Background Birth of Louis Brown (July 25, 1978)
Mother had blocked fallopian tubes
First IVF baby

4. In-Vitro Fertilization
In-Vitro Fertilization (IVF)
Fertilization step completed in the test tube
Early embryo culture
Place in uterus for further
development

5. In-Vitro Fertilization
High pregnancy rates but also high multiple pregnancy rates:
NTUH: Pregnancy 60%; > 1 baby ~ 30%
Tiitinen et al. (2003): 34%; >1 baby ~25%

6. Problem
How to optimally select the embryo with the highest potential for pregnancy? ?

7. Classical Method Morphological analysis Number of cells
Quality of cells

8. Classical Method
Morphological analysis

9. Classical Method
Morphological analysis

10. Classical Method

11. Competing Methodologies

12. Time Lapse PubMed: > 7900 Used in embryo research for a century
First: Lewis & Gregory, 1929 (Science, 69)
First in human: Eriksson et al. 1981
Second: Payne et al. 1997

15. Data Structure
Phase contrast technique called Hoffman Modulation Contrast (HMC):
transparent embryos and their substructures have a complex, 3D-like sidelit appearance
Multiple focal planes
Multiple time points

16. Current Methods
Morphokinetic study
Vitrolife
Embryoscope

17. Vitrolife
Embryoscope

19. EEVA system

21. Variational Segmentation
IEEE conference paper, 2004

22. Variational Segmentation
IEEE conference paper, 2004

26. Time consuming and require expertise
水平集,階段一致性　　曲線擬合
Using level set, phase congruency and fitting of ellipse methods  evaluate blastocyst extension (BE), inner cell mass (ICM), Trophectoderm area (TE)  classifier
BE: 67% to 92%
ICM: 67% to 82%
TE: 53% to 92%
Time consuming and require expertise

29. Deep Learning
10,148 de-identified embryos at 110 hpi (hours post-insemination)
Taken at seven focal depths (+45, +30, +15, 0, -15, -30, and -45)  50,392 total images
Classed into 3 groups: good-quality, fair-quality, poor-quality)
Inception-V1 deep learning-based algorithm

30. Deep Learning
Evaluated performance using a randomly selected independent test set:
964 good-quality embryo images (141 embryos)
966 poor-quality embryo images (142 embryos)
96.94% accuracy (1,871 correct predictions out of 1,930 images)
Voting system  final assessment of embryo
97.53% accuracy (276 correct predictions out of 283 embryos)

31. Accuracy: 90.4%
Precision: 95.7%

32. Aspiration
Morphological analyses of multi-focal, multi-temporal datasets
Minimal user input of labels and features
Create an objective benchmark to assess against clinical results
Ability to search for similar images through the embryo database