Human blastocyst segmentation using neural network

Kheradmand, Shakiba; Saeedi, Parvaneh; Bajić, Ivan V.

doi:10.1109/ccece.2016.7726763

Cited by 17 publications

(6 citation statements)

References 5 publications

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“…Kheradmand et al detected the three components of embryo ZP, TE, and ICM using the neural network approach. In detail, they used discrete cosine transform for the segmentation process, and a two-layered neural network is used to predict the component properties [ 29 ]. Saeedi et al all provided the publicly available human embryo dataset with multi-class labeling by expert embryologists.…”

Section: Background and Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by In Vitro Fertilization

Mushtaq¹,

Mumtaz²,

Raza³

et al. 2022

Sensors

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Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. In vitro fertilization (IVF) is one of those assisted reproduction methods in which the sperm and eggs are combined outside the human body in a specialized environment and kept for growth. Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. The morphology of the embryological components is highly related to the success of the assisted reproduction procedure. In approximately 3–5 days, the embryo transforms into the blastocyst. To prevent the multiple-birth risk and to increase the chance of pregnancy the embryologist manually analyzes the blastocyst components and selects valuable embryos to transfer to the women’s uterus. The manual microscopic analysis of blastocyst components, such as trophectoderm, zona pellucida, blastocoel,and inner cell mass, is time-consuming and requires keen expertise to select a viable embryo. Artificial intelligence is easing medical procedures by the successful implementation of deep learning algorithms that mimic the medical doctor’s knowledge to provide a better diagnostic procedure that helps in reducing the diagnostic burden. The deep learning-based automatic detection of these blastocyst components can help to analyze the morphological properties to select viable embryos. This research presents a deep learning-based embryo component segmentation network (ECS-Net) that accurately detects trophectoderm, zona pellucida, blastocoel, and inner cell mass for embryological analysis. The proposed method (ECS-Net) is based on a shallow deep segmentation network that uses two separate streams produced by a base convolutional block and a depth-wise separable convolutional block. Both streams are densely concatenated in combination with two dense skip paths to produce powerful features before and after upsampling. The proposed ECS-Net is evaluated on a publicly available microscopic blastocyst image dataset, the experimental segmentation results confirm the efficacy of the proposed method. The proposed ECS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% for embryological analysis.

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Section: Background and Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by In Vitro Fertilization

Mushtaq¹,

Mumtaz²,

Raza³

et al. 2022

Sensors

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show abstract

“…They used patches as input to the model, and a refinement approach based on self-supervision was employed for performance enhancement [30]. Kheradmand et al (2016) proposed a CNN-based approach for the automatic detection of potential blastocyst components. Blastocyst components were detected using edge detection with preprocessing.…”

Section: Introductionmentioning

confidence: 99%

Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro Fertilization

Ishaq¹,

Raza²,

Rehar³

et al. 2023

Mathematics

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The increasing global infertility rate is a matter of significant concern. In vitro fertilization (IVF) significantly minimizes infertility by providing an alternative clinical means of becoming pregnant. The success of IVF mainly depends on the assessment and analysis of human blastocyst components such as the blastocoel (BC), zona pellucida (ZP), inner cell mass (ICM), and trophectoderm (TE). Embryologists perform a morphological assessment of the blastocyst components for the selection of potential embryos to be used in the IVF process. Manual assessment of blastocyst components is time-consuming, subjective, and prone to errors. Therefore, artificial intelligence (AI)-based methods are highly desirable for enhancing the success rate and efficiency of IVF. In this study, a novel feature-supplementation-based blastocyst segmentation network (FSBS-Net) has been developed to deliver higher segmentation accuracy for blastocyst components with less computational overhead compared with state-of-the-art methods. FSBS-Net uses an effective feature supplementation mechanism along with ascending channel convolutional blocks to accurately detect the pixels of the blastocyst components with minimal spatial loss. The proposed method was evaluated using an open database for human blastocyst component segmentation, and it outperformed state-of-the-art methods in terms of both segmentation accuracy and computational efficiency. FSBS-Net segmented the BC, ZP, ICM, TE, and background with intersections over union (IoU) values of 89.15, 85.80, 85.55, 80.17, and 95.61%, respectively. In addition, FSBS-Net achieved a mean IoU for all categories of 87.26% with only 2.01 million trainable parameters. The experimental results demonstrate that the proposed method could be very helpful in assisting embryologists in the morphological assessment of human blastocyst components.

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“…Kheradmand et al proposed a neural network-based approach to detect ZP, TE, and ICM areas in blastocyst images. They used preprocessing and edge detection to detect the components [21]. A similar group presented a deep learning-based segmentation method to detect ICM from blastocyst images.…”

Section: Introductionmentioning

confidence: 99%

Detecting Blastocyst Components by Artificial Intelligence for Human Embryological Analysis to Improve Success Rate of In Vitro Fertilization

Arsalan

Haider

Choi

et al. 2022

JPM

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Morphological attributes of human blastocyst components and their characteristics are highly correlated with the success rate of in vitro fertilization (IVF). Blastocyst component analysis aims to choose the most viable embryos to improve the success rate of IVF. The embryologist evaluates blastocyst viability by manual microscopic assessment of its components, such as zona pellucida (ZP), trophectoderm (TE), blastocoel (BL), and inner cell mass (ICM). With the success of deep learning in the medical diagnosis domain, semantic segmentation has the potential to detect crucial components of human blastocysts for computerized analysis. In this study, a sprint semantic segmentation network (SSS-Net) is proposed to accurately detect blastocyst components for embryological analysis. The proposed method is based on a fully convolutional semantic segmentation scheme that provides the pixel-wise classification of important blastocyst components that help to automatically check the morphologies of these elements. The proposed SSS-Net uses the sprint convolutional block (SCB), which uses asymmetric kernel convolutions in combination with depth-wise separable convolutions to reduce the overall cost of the network. SSS-Net is a shallow architecture with dense feature aggregation, which helps in better segmentation. The proposed SSS-Net consumes a smaller number of trainable parameters (4.04 million) compared to state-of-the-art methods. The SSS-Net was evaluated using a publicly available human blastocyst image dataset for component segmentation. The experimental results confirm that our proposal provides promising segmentation performance with a Jaccard Index of 82.88%, 77.40%, 88.39%, 84.94%, and 96.03% for ZP, TE, BL, ICM, and background, with residual connectivity, respectively. It is also provides a Jaccard Index of 84.51%, 78.15%, 88.68%, 84.50%, and 95.82% for ZP, TE, BL, ICM, and background, with dense connectivity, respectively. The proposed SSS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% and 86.34% with residual and dense connectivity, respectively; this shows effective segmentation of blastocyst components for embryological analysis.

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Human blastocyst segmentation using neural network

Cited by 17 publications

References 5 publications

Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by In Vitro Fertilization

Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by In Vitro Fertilization

Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro Fertilization

Detecting Blastocyst Components by Artificial Intelligence for Human Embryological Analysis to Improve Success Rate of In Vitro Fertilization

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