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

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

doi:10.3390/jpm12020124

Cited by 22 publications

(10 citation statements)

References 43 publications

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“…The morphological properties of blastocyst components are extremely important for determining embryo viability [ 6 , 21 , 37 ]. The MASS-Net effectively detects these components in a multiclass scenario.…”

Section: Discussionmentioning

confidence: 99%

“…These images were captured for different patients between 2012 and 2016 using an Olympus IX71 (Olympus Corp., Tokyo, Japan) inverted microscope and Research Instrument Cronus 4 software (Research Instruments, Falmouth, England) at the Pacific Center for Reproduction Canada. In addition, to realize a fair comparison with the state-of-the-art learning-based method on the same dataset, the train-test split method mentioned in [ 21 , 22 ] was enforced. Out of a total of 235 images, 200 (85%) and 35 (15%) were used for training and testing, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, to evaluate the segmentation performance, the output masks of MASS-Net were pixel-wise compared with expert annotation (provided by an expert embryologist), and a versatile Jaccard index (JI) was used to compute the performance. MASS-Net is a learning-based method; therefore, following [ 21 , 22 ], JI was used to fairly compare the proposed method with state-of-the-art methods on the same dataset and training-testing protocols. The JI is expressed Equation (2): where true-positive (TP) is a pixel that is predicted as an embryo component pixel and embryo pixel in the expert annotation.…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…Moreover, it has been pointed out that the AI was more probably to result from misinterpretations in patients with coexisting polypoid tumors (or) benign leiomyomas [29]. With the individual variations of cancer patients and the multidrug conflict development, numerous patients with gynecological cancer have poor drug sensitivity follow-on unacceptable clinical treatment [32].…”

Section: Research Gapmentioning

confidence: 99%

A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology

Malani¹,

Shrivastava²,

Raka³

2023

Cureus

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The exponential growth of artificial intelligence (AI) has fascinated its application in various fields and so in the field of healthcare. Technological advancements in theories and learning algorithms and the availability of processing through huge datasets have created a breakthrough in the medical field with computing systems. AI can potentially drive clinicians and practitioners with appropriate decisions in managing cases and reaching a diagnosis, so its application is extensively spread in the medical field. Thus, computerized algorithms have made predictions so simple and accurate. This is because AI can proffer information accurately even to many patients. Furthermore, the subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. Despite numerous challenges, AI implementation in obstetrics and gynecology is found to have a spellbound development. Therefore, this review propounds exploring the implementation of AI in obstetrics and gynecology to improve the outcomes and clinical experience. In that context, the evolution and progress of AI, the role of AI in ultrasound diagnosis in distinct phases of pregnancy, clinical benefits, preterm birth postpartum period, and applications of AI in gynecology are elucidated in this review with future recommendations.

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“…In recent years, artificial intelligence has made significant contributions to automating manual processes (Arsalan et al, 2022c;Haider et al, 2022a;Mahmood et al, 2022b). In particular, the combination of deep learning with computer vision has enabled complex problems to be solved using multimedia-based learning (Owais et al, 2021;Sultan et al, 2021;Arsalan et al, 2022a). Moreover, convolutional neural networks (CNNs) supported by machine vision have been used in automated marine observation.…”

Section: Introductionmentioning

confidence: 99%

Robust segmentation of underwater fish based on multi-level feature accumulation

Haider

Arsalan²,

Choi³

et al. 2022

Front. Mar. Sci.

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Because fish are vital to marine ecosystems, monitoring and accurate detection are crucial for assessing the potential for fisheries in these environments. Conventionally, fish-related assessment is conducted manually, which makes it labor-intensive and time-consuming. In addition, the assessments are challenging owing to underwater visibility limitations, which leads to poor detection accuracy. To overcome these problems, we propose two novel architectures for the automatic and high-performance segmentation of fish populations. In this study, the efficient fish segmentation network (EFS-Net) and multi-level feature accumulation-based segmentation network (MFAS-Net) are the base and final networks, respectively. In deep convolutional neural networks, the initial layers usually contain potential spatial information. Therefore, the EFS-Net employs a series of convolution layers in the early stage of the network for optimal feature extraction. To boost segmentation accuracy, the MFAS-Net uses an initial feature refinement and transfer block to refine potential low-level information and subsequently transfers it to the deep stages of the network. Moreover, the MFAS-Net employs multi-level feature accumulation that improves pixel-wise prediction for fish that are indistinct. The proposed networks are evaluated using two publicly available datasets, namely DeepFish and semantic segmentation of underwater imagery (SUIM), both of which contain challenging underwater fish segmentation images. The experimental results reveal that mean intersection-over-unions of 76.42% and 92.0% are attained by the proposed method for the DeepFish and SUIM datasets, respectively; these values are higher than those by the state-of-the-art methods such as A-LCFCN+PM and DPANet. In addition, high segmentation performance is achieved without compromising the computational efficiency of the networks. The MFAS-Net requires only 3.57 million trainable parameters to be fully trained. The proposed model and the complete code will be made available1.

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

Cited by 22 publications

References 43 publications

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

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

A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology

Robust segmentation of underwater fish based on multi-level feature accumulation

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