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

Muhammad Arsalan,Jiho Choi,Kang Ryoung Park,Adnan Haider

doi:10.3390/jpm12020124

Abstract

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.

Highlights

Infertility is a major clinical condition and a serious concern that affects 8–12% of couples, accounting for approximately 80 million couples worldwide [1]
Considering the advancement of deep learning and its benefits in computer-aided diagnosis [32,33,34], this study proposes a novel sprint semantic segmentation network (SSS-Net) that accurately detects the blastocyst components (ZP, TE, BL, and inner cell mass (ICM)) for embryological analysis and improves the success rate of In vitro fertilization (IVF)
Where TP represents true positive

Summary

Introduction

Infertility is a major clinical condition and a serious concern that affects 8–12% of couples, accounting for approximately 80 million couples worldwide [1]. Estimates indicate that 6.1 million people are affected by infertility in the United States, and only half of them are undergoing fertility-related treatments [3]. PSP-Net [38] 35 M DeepLab V3 [39] 40 M Blast-Net [24] 25 M. The methods of UNet-Baseline [36], TernausNet U-Net [37], PSP-Net [38], and DeepLab V3 [39] are those that were designed for different tasks (other than blastocyst segmentation), but these methods are implemented by [24] using the same train-test criteria and protocols.

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Methods

Results

Discussion

Conclusion