Automatic segmentation of trophectoderm in microscopic images of human blastocysts

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AUTOMATIC SEGMENTATION OF TROPHECTODERM IN MICROSCOPIC
IMAGES OF HUMAN BLASTOCYSTS
By
A
PROJECT REPORT
Submitted to the Department of electronics &communication Engineering in the
FACULTY OF ENGINEERING & TECHNOLOGY
In partial fulfillment of the requirements for the award of the degree
Of
MASTER OF TECHNOLOGY
IN
ELECTRONICS &COMMUNICATION ENGINEERING
APRIL 2016

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CERTIFICATE
Certified that this project report titled “Automatic Segmentation of Trophectoderm in
Microscopic Images of Human Blastocysts” is the bonafide work of Mr. _____________Who
carried out the research under my supervision Certified further, that to the best of my knowledge
the work reported herein does not form part of any other project report or dissertation on the basis
of which a degree or award was conferred on an earlier occasion on this or any other candidate.
Signature of the Guide Signature of the H.O.D
Name Name

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DECLARATION
I hereby declare that the project work entitled “Automatic Segmentation of Trophectoderm in
Microscopic Images of Human Blastocysts” Submitted to BHARATHIDASAN UNIVERSITY
in partial fulfillment of the requirement for the award of the Degree of MASTER OF APPLIED
ELECTRONICS is a record of original work done by me the guidance of Prof.A.Vinayagam
M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any
other thesis or work on the basis of which a degree or award was conferred on an earlier occasion
to me or any other candidate.
(Student Name)
(Reg.No)
Place:
Date:

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ACKNOWLEDGEMENT
I am extremely glad to present my project “Automatic Segmentation of Trophectoderm in
Microscopic Images of Human Blastocysts” which is a part of my curriculum of third semester
Master of Science in Computer science. I take this opportunity to express my sincere gratitude to
those who helped me in bringing out this project work.
I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.),
PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project.
I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from
my deep heart for her valuable comments I received through my project.
I wish to express my deep sense of gratitude to my guide
Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for
successful completion of this project.
I also express my sincere thanks to the all the staff members of Computer science for their kind
advice.
And last, but not the least, I express my deep gratitude to my parents and friends for their
encouragement and support throughout the project.

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ABSTRACT:
Accurate assessment of embryos viability is an extremely important task in the
optimization of in vitro fertilization treatment outcome. One of the common ways of assessing the
quality of a human embryo is grading it on its fifth day of development based on morphological
quality of its three main components (Trophectoderm, Inner Cell Mass, and the level of expansion
or the thickness of its Zona Pellucida). In this study, we propose a fully automatic method for
segmentation and measurement of TE region of blastocysts (day-5 human embryos). Here, we
eliminate the inhomogeneities of the blastocysts surface using the Retinex theory and further apply
a level-set algorithm to segment the TE regions. We have tested our method on a dataset of 85
images and have been able to achieve a segmentation accuracy of 84.6% for grade A, 89.0% for
grade B, and 91.7% for grade C embryos.

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INTRODUCTION:
The process of in vitro fertilization (IVF) has evolved considerably since the first
successful treatment three decades ago. However, the efficiency remains somewhat questionable,
with most patients requiring more than one treatment cycle to succeed. This is partly related to the
high variability in developmental competence of the embryos produced during an IVF cycle and
difficulties in determining which of the generated embryos has the highest potential leading to live
birth. For this reason, IVF clinics across the world often transfer more than one embryo per cycle
to increase the odds that a viable embryo will be transferred. While this approach has helped to
maintain the current IVF’s pregnancy rates, it has also led to large numbers of multiple pregnancies
(MP). The potentially negative consequences of MPs include preeclampsia, maternal
haemorrhage, operative delivery, uterine rupture, and preterm labor. MPs can easily be prevented
by transferring fewer embryos to the patient’s uterus, if the quality of embryos can be determined
precisely and therefore transferring only the embryo with the highest implantation potential.
Researchers are continuously making efforts toward developing techniques and measures
that enhance the chance of selecting more viable embryos. These efforts have led to the
development of grading systems that are used to bench mark embryos into different categories
according to their potentials in leading to live birth. A morphological grading system crafted by
Gardner and Schoolcraft is widely adopted by IVF clinics for selecting the embryo with highest
quality based on three morphological measures . Despite the availability of such scoring metric, it
is very difficult to choose one embryo over its sibling embryos without knowing the relative
contribution of each parameter. Experiments by Ahlstrom¨ et al. proved the superiority of
Trophectoderm (TE) over the other measures, which can be further used to select the embryo(s)
with highest implantation potential from a group of embryos. The study also proved an essential
need for a strong TE layer for successful hatching of an implanted embryo. Hence, it is extremely
important and necessary to analyze the TE’s quality to assess the quality of an embryo at the
blastocyst stage
In the past, medical professionals have manually analyzed human embryos and further
guessed the probability of live birth associated with such embryos. Bendus et al. proved that the
involvement of human embryologists may lead to significant difference between the scores
allocated to a set of embryos by different embryologists. It is impossible to avoid such difference

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in these scores resulting in a direct impact on the likelihood of IVF success. This difference can
potentially be avoided by taking aid from automatic methods that are capable of computing more
robust scores. A number of attempts have been made in the past to analyze human embryos using
semiautomatic techniques. Hnida et al. presented a semiautomatic software to analyze human
embryo’s morphology. FertiMorph, a semiautomatic system by IHMedical A/S, Copenhagen,
Denmark, was proposed for analyzing the blastocyst size in a sequence of embryo images. Giusti
et al. presented an approach for segmentation of day- 2 embryos with an energy value linked to
each stage. They used a variational level-set algorithm proposed by Li et al. To segment the TE’s
inner boundaries.
Although many semiautomatic methods have been developed in the past, full automaton
has yet not been achieved due to the complex nature of patterns and shape of different components
of an embryo at different stages. In addition, a significant error can occur in automatic
identification due to the image quality and debris in the neighborhood of an embryo’s growing
environment. In this paper, we propose a robust and automatic segmentation method for TE region
that performs accurately even if there are other embryos or debris in the vicinity of the embryo of
interest and seen in the background. Our algorithm does not require any manual preprocessing for
removing such cells or particles and can still deliver good results. We utilize the Retinex algorithm
to deemphasize cells in the cavity area and further apply a level-set algorithm to segment TE
regions in the blastocyst image. The superiority of our algorithm over previous work is
demonstrated using blastocyst images of all TE grades
The paper is divided into the following sections. Section II describes the blastocyst grading
system. The previous work in computer-based embryo image processing and analysis is also
presented in that section. Section III explains details of the proposed segmentation algorithm.
Section IV overviews our image acquisition device and its settings as used for capturing our dataset
images. Section V describes the ground truth (GT) establishment. Section VI details related
parameters and the experimental results on our database of Hoffman Modulation Contrast (HMC)
embryo images. It also provides performancerelated details. Finally, Section VII summarizes the
conclusions and future challenges.

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CONCLUSION:
In this study, we presented a novel algorithm for identification of Trophectoderm regions
of human embryos on their fifth day of creation. We utilized the Retinex algorithm to enhance the
quality of our input images by deemphasizing blastocyst cavity regions and those areas outside the
embryo. We then used the level-set algorithm that did not require manual initialization and
automatically converges to the TE boundaries. The proposed system can effectively and
automatically segment TE regions in blastocyst images of grades A, B, and C. We require only
one condition in which the size of the embryo of interest must be larger than the remaining embryos
in the image. As demonstrated, the proposed algorithm can detect TE regions with an average
shape accuracy of 87.8%. To our best knowledge, the presented results here are the best reported
results for the segmentation of Trophectoderm regions.

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REFERENCES:
[1] A. Ahlstrom, C. Westin, E. Reismer, M. Wikland, and T. Hardarson, ¨ “Trophectoderm
morphology: An important parameter for predicting live birth after single blastocyst tran,” Human
Reproduction, vol. 26, no. 12, pp. 3289–3296, 2011.
[2] A. E. B. Bendus, J. F. Mayer, S. K. Shipley, and W. H. Catherino, “Interobserver and
intraobserver variation in day 3 embryo grading,” Fertility Sterility, vol. 86, no. 6, pp. 1608–1615,
2006.
[3] A. Giusti, G. Corani, L. Gambardella, C. Magli, and L. Gianaroli, “Blastomere segmentation
and 3D morphology measurements of early embryos from Hoffman modulation contrast image
stacks,” in Proc. IEEE Int. Conf. Biomed. Imag., From Nano Macro, 2010, pp. 1261–1264.
[4] J.-M. Morel, A. B. Petro, and C. Sbert, “A PDE formalization of retinex theory,” IEEE Trans.
Image Process., vol. 19, no. 11, pp. 2825–2837, Nov. 2010.
[5] C. Li, R. Huang, Z. Ding, J. Gatenby, N. Metaxas, and J. Gore, “A level set method for image
segmentation in the presence of intensity inhomogeneities with application to MRI,” IEEE Trans.
Image Process., vol. 20, no. 7, pp. 2007–2016, Jul. 2011.
[6] D. Morales, E. Bengoetxea, and P. Larraaga, “Automatic segmentation of zona pellucida in
human embryo images applying an active contour model,” in Proc. Med. Image Understanding
Anal., 2008, pp. 209–213.
[7] C. Wong, K. Loewke, N. Bossert, B. Behr, C. De Jonge, T. Baer, and R. Reijo Pera, “Non-
invasive imaging of human embryos before embryonic genome activation predicts development
to the blastocyst stage,” in Nat. Biotechnol., vol. 28, no. 10, 2010, pp. 1115–21.

Automatic segmentation of trophectoderm in microscopic images of human blastocysts

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Automatic segmentation of trophectoderm in microscopic images of human blastocysts