automatic blastomere and trophectoderm extraction
TRANSCRIPT
Automatic Methods for Human Embryo Component Extraction
Laboratory for Robotic Vision School of Engineering Science Simon Fraser University
Amarjot Singh
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Content• Motivation • Need for Automation • Embryo Grading • Related Work • Proposed Algorithms
➢ Blastomere Extraction ➢ Trophectoderm Segmentation
• Experimental Results • Future Work
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Motivation• Economic conditions and pursuit of advanced careers have influenced
women to defer childbearing. • Unfortunately, female reproductive capacity declines in the 30s. • In-vitro fertilisation (IVF) achieves successful pregnancies by fertilizing an
egg with a sperm developed during Day 1 to Day 5 IVF process.
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Need for Automation• Currently, the quality of embryos is manually analyzed because of which the
implantation rates for IVF embryos remain relatively low at a 30% a clinical
pregnancy rate.
• In addition, high variability in developmental competence of the embryos
adds to lower clinical pregnancy rate.
• IVF clinics across the world often transfer more than one embryo per cycle
to increase the odds that can led to MP.
• An automatic method that will allow less skilled embryologists to assess the
quality of embryos with the help of automated systems.
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Embryo Grading• It is important to understand the
parameters used for embryo grading for developing the automatic system.
• Embryo is graded on: 1. Embryo (Day 1-3)
➢ Blastomere size and shape (1-4).
2. Blastocyst (Day 5) ➢ ZP expansion (1-6). ➢ Trophectoderm width (a-c). ➢ ICM compactness (A-C).
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Related Work - Blastomeres• Few attempts made in the past using:
• Lasers. (Pederson et al.) • Imaging. (Guisti et al.)
• Difficult Problem as: • Fragmentation. • Illumination variation. • Overlapping cells. • Number of cells. • Size of cells.
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Related Work - Trophectoderm• No Fully Automatic Method.
• One semi-automatic method:
• Imaging. (Filho et al.)
• Difficult Problem as:
• Visual artifacts inside cavity.
• TE and ICM connected.
• Defocused Images.
Proposed Algorithms• The proposed algorithms identify
Blastomeres (Day 1-2) and
Trophectoderm (Day-5).
• These components can be used to
make an automatic embryo grading
system.
• These systems will allow
embryologists to assess the quality
of an embryo at different stages.
Blastomeres
Trophectoderm8
Blastomere Extraction
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w = k*exp((I-J).^2)
Region Merging
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Ellipse Fitting and Refinement• Anisotropic image smoothing. • Edges extracted using hessian
edge operator. • Least square ellipse fitting on
image edges. • Remove ellipses that are:
➢ Large. ➢ Similar. ➢ Contained and Disjoint.
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Blastomere Extraction• Fit ellipse on refined image regions. • Remove regions with no ellipse
overlap. • Generate equilibriums within 10
pixels distance from the embryo centroid.
• Select the best equilibrium based on edge overlap.
• Best equilibrium set corresponds to the blastomeres.
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Overview of the Algorithm
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Qualitative Results
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Quantitative Results
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Time complexity
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Trophectoderm Segmentation
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Level-Set Algorithm
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Morphology• Extract level-set edge output
using canny detector.
• Dilate to connect discontinuous
edges.
• Draw radial beams from the
embryo center.
• Select segments equidistant from
the embryo center.
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TE Separation from ICM• Pixels with high standard.
deviation width are removed using K-Means.
• Blue shows TE cluster. • Red shows partial ICM cluster. • Red cluster is removed and
replaced with pixel values obtained by convergence of snake in the vicinity.
• TE boundary is smoothened and overlaid in red.
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Overview of the Algorithm
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Qualitative Results
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Quantitative Results
Blastocyst Grade
Mean Shape Accuracy
Mean Correctness
Mean Completeness
Mean Quality
a 84.6 79.8 74.2 67.6
b 88.9 85.5 82.3 76.8
c 91.7 84.6 78.4 72.3
Combined 87.7 83.3 78.7 72.7
The algorithm has an average computational complexity of 153 sec.
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Future Work• Blastomere Extraction
• Use a more robust ellipse fitting algorithm (Ransac) to avoid ellipse
outliers.
• Use of a more sophisticated edge detector can result into better
blastomere extraction.
• Trophectoderm Segmentation
• Use of texture feature with the gradient feature can improve the TE
segmentation accuracy.
• Finally, an enhanced K-Means as opposed to a standard version will
produce better separation between the TE and ICM.
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Thank You
Any Questions?
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