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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