Choosing an embryo for transfer

Kovács, Péter; Mátyás, Szabolcs

doi:10.1002/9781119622215.ch76

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…The box counting-based method is a common type of algorithm for calculating the FD [43][44][45], and was implemented by Python (version 3.8). The following is the formula for calculating the FD using the box-counting method: FD = lim p→0 log(N(p)) log(1/p) (5) where N(p) denotes the total count of boxes with size "p" needed to entirely cover the curve. The parameter "p" represents the box size, while FD indicates the fractal dimension that characterizes the examined curve.…”

Section: The Computation Of Fractal Dimensionmentioning

confidence: 99%

“…To enhance the chances of a successful gestation, the number of embryo transfers and quality assessments of these embryos are crucial. Morphological scoring is used to determine an embryo's viability prior to transfer into the uterus [5]. Moreover, IVF involves the risk of multiple pregnancies, which is a threat to the mother and baby and results in several risks.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Detection of Human Blastocyst Compartments with Fractal Dimension Estimation

Arsalan,

Haider,

Hong

et al. 2024

Fractal Fract

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In vitro fertilization (IVF) is an efficacious form of aided reproduction to deal with infertility. Human embryos are taken from the body, and these are kept in a supervised laboratory atmosphere during the IVF technique until they exhibit blastocyst properties. A human expert manually analyzes the morphometric properties of the blastocyst and its compartments to predict viability through manual microscopic evaluation. A few deep learning-based approaches deal with this task via semantic segmentation, but they are inaccurate and use expensive architecture. To automatically detect the human blastocyst compartments, we propose a parallel stream fusion network (PSF-Net) that performs the semantic segmentation of embryo microscopic images with inexpensive shallow architecture. The PSF-Net has a shallow architecture that combines the benefits of feature aggregation through depth-wise concatenation and element-wise summation, which helps the network to provide accurate detection using 0.7 million trainable parameters only. In addition, we compute fractal dimension estimation for all compartments of the blastocyst, providing medical experts with significant information regarding the distributional characteristics of blastocyst compartments. An open dataset of microscopic images of the human embryo is used to evaluate the proposed approach. The proposed method also demonstrates promising segmentation performance for all compartments of the blastocyst compared with state-of-the-art methods, achieving a mean Jaccard index (MJI) of 87.69%. The effectiveness of PSF-Net architecture is also confirmed with the ablation studies.

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Section: The Computation Of Fractal Dimensionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Detection of Human Blastocyst Compartments with Fractal Dimension Estimation

Arsalan,

Haider,

Hong

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

show abstract

“…The morphological attributes and formation of the specific embryo compartments are the indications of the embryo reaching the blastocyst stage. Based on these formations and developmental capabilities the transfer of viable embryos can increase the chance of pregnancy by IVF [ 5 ]. Conventionally, embryologists use time-lapse microscopic analysis to assess the specific composition of the blastocyst and the proper formation of its specific compartments.…”

Section: Introductionmentioning

confidence: 99%

Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic Analysis

et al. 2022

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Infertility is one of the most important health concerns worldwide. It is characterized by not being successful of pregnancy after some periods of periodic unprotected sexual intercourse. In vitro fertilization (IVF) is an assisted reproduction technique that efficiently addresses infertility. IVF replaces the actual mode of reproduction through a manual procedure wherein embryos are cultivated in a controlled laboratory environment until they reach the blastocyst stage. The standard IVF procedure includes the transfer of one or two blastocysts from several blastocysts that are grown in a controlled environment. The morphometric properties of blastocysts with their compartments such as trophectoderm (TE), zona pellucida (ZP), inner cell mass (ICM), and blastocoel (BL), are analyzed through manual microscopic analysis to predict viability. Deep learning has been extensively used for medical diagnosis and analysis and can be a powerful tool to automate the morphological analysis of human blastocysts. However, the existing approaches are inaccurate and require extensive preprocessing and expensive architectures. Thus, to cope with the automatic detection of blastocyst components, this study proposed a novel multiscale aggregation semantic segmentation network (MASS-Net) that combined four different scales via depth-wise concatenation. The extensive use of depthwise separable convolutions resulted in a decrease in the number of trainable parameters. Further, the innovative multiscale design provided rich spatial information of different resolutions, thereby achieving good segmentation performance without a very deep architecture. MASS-Net utilized 2.06 million trainable parameters and accurately detects TE, ZP, ICM, and BL without using preprocessing stages. Moreover, it can provide a separate binary mask for each blastocyst component simultaneously, and these masks provide the structure of each component for embryonic analysis. Further, the proposed MASS-Net was evaluated using publicly available human blastocyst (microscopic) imaging data. The experimental results revealed that it can effectively detect TE, ZP, ICM, and BL with mean Jaccard indices of 79.08, 84.69, 85.88%, and 89.28%, respectively, for embryological analysis, which was higher than those of the state-of-the-art methods.

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Choosing an embryo for transfer

Cited by 2 publications

References 22 publications

Deep Learning-Based Detection of Human Blastocyst Compartments with Fractal Dimension Estimation

Deep Learning-Based Detection of Human Blastocyst Compartments with Fractal Dimension Estimation

Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic Analysis

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