{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:02:06Z","timestamp":1791914526345,"version":"4.3.4"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2021R1F1A1045587"],"award-info":[{"award-number":["NRF-2021R1F1A1045587"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020R1A2C1006179"],"award-info":[{"award-number":["NRF-2020R1A2C1006179"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2021-2020-0-01789"],"award-info":[{"award-number":["IITP-2021-2020-0-01789"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jpm12020124","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T08:09:20Z","timestamp":1642493360000},"page":"124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Detecting Blastocyst Components by Artificial Intelligence for Human Embryological Analysis to Improve Success Rate of In Vitro Fertilization"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1868-5207","authenticated-orcid":false,"given":"Muhammad","family":"Arsalan","sequence":"first","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2240-2708","authenticated-orcid":false,"given":"Adnan","family":"Haider","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiho","family":"Choi","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang Ryoung","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.cegh.2020.08.008","article-title":"Prevalence and Potential Determinants of Primary Infertility in India: Evidence from Indian Demographic Health Survey","volume":"9","author":"Purkayastha","year":"2021","journal-title":"Clin. Epidemiol. Glob. Health"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lepore, M., and Petruzziello, A. (2021, January 14\u201321). A Situation-Aware DSS to Support Assisted Reproductive Technology Outcome Prediction. Proceedings of the IEEE Conference on Cognitive and Computational Aspects of Situation Management, Tallin, Estonia.","DOI":"10.1109\/CogSIMA51574.2021.9475933"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"61.e1","DOI":"10.1016\/j.ajog.2021.02.015","article-title":"Infertility in the Midwest: Perceptions and Attitudes of Current Treatment","volume":"225","author":"Galic","year":"2021","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.fertnstert.2020.09.157","article-title":"Artificial Intelligence in Human in Vitro Fertilization and Embryology","volume":"114","author":"Zaninovic","year":"2020","journal-title":"Fertil. Steril."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.fertnstert.2013.02.056","article-title":"In Vitro Fertilization with Single Euploid Blastocyst Transfer: A Randomized Controlled Trial","volume":"100","author":"Forman","year":"2013","journal-title":"Fertil. Steril."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.cryobiol.2021.04.003","article-title":"Blastocyst Transfer after Extended Culture of Cryopreserved Cleavage Embryos Improves in Vitro Fertilization Cycle Outcomes","volume":"100","author":"Le","year":"2021","journal-title":"Cryobiology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.bpobgyn.2018.07.001","article-title":"Single Embryo Transfer for All","volume":"53","author":"Cutting","year":"2018","journal-title":"Best Pract. Res. Clin. Obstet. Gynaecol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ejogrb.2021.10.004","article-title":"Single Blastocyst Stage versus Single Cleavage Stage Embryo Transfer Following Fresh Transfer: A Systematic Review and Meta-Analysis","volume":"267","author":"Li","year":"2021","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.rbmo.2017.06.022","article-title":"Time-Lapse Culture with Morphokinetic Embryo Selection Improves Pregnancy and Live Birth Chances and Reduces Early Pregnancy Loss: A Meta-Analysis","volume":"35","author":"Pribenszky","year":"2017","journal-title":"Reprod. Biomed. Online"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.rbmo.2020.09.031","article-title":"An Artificial Intelligence Model Based on the Proteomic Profile of Euploid Embryos and Blastocyst Morphology: A Preliminary Study","volume":"42","author":"Bori","year":"2021","journal-title":"Reprod. Biomed. Online"},{"key":"ref_11","first-page":"73","article-title":"Effects of Abnormal Zona Pellucida on Fertilization and Pregnancy in IVF\/ICSI-ET","volume":"26","author":"Zhao","year":"2015","journal-title":"J. Reprod. Contracept."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s10815-021-02110-7","article-title":"Blastocyst Age, Expansion, Trophectoderm Morphology, and Number Cryopreserved Are Variables Predicting Clinical Implantation in Single Blastocyst Frozen Embryo Transfers in Freeze-Only-IVF","volume":"38","author":"Ozgur","year":"2021","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/s41598-018-36452-7","article-title":"Identification of Extracellular Vesicles and Characterization of MiRNA Expression Profiles in Human Blastocoel Fluid","volume":"9","author":"Battaglia","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.4103\/0366-6999.232808","article-title":"Overall Blastocyst Quality, Trophectoderm Grade, and Inner Cell Mass Grade Predict Pregnancy Outcome in Euploid Blastocyst Transfer Cycles","volume":"131","author":"Zhao","year":"2018","journal-title":"Chin. Med. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1016\/j.rbmo.2021.08.008","article-title":"Focus on Time-Lapse Analysis: Blastocyst Collapse and Morphometric Assessment as New Features of Embryo Viability","volume":"43","author":"Sciorio","year":"2021","journal-title":"Reprod. Biomed. Online"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1002\/rmb2.12302","article-title":"Selection of High-Quality and Viable Blastocysts Based on Timing of Morula Compaction and Blastocyst Formation","volume":"19","author":"Harada","year":"2020","journal-title":"Reprod. Med. Biol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1038\/nbt.1686","article-title":"Non-Invasive Imaging of Human Embryos before Embryonic Genome Activation Predicts Development to the Blastocyst Stage","volume":"28","author":"Wong","year":"2010","journal-title":"Nat. Biotechnol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1109\/TBME.2014.2356415","article-title":"Automatic Segmentation of Trophectoderm in Microscopic Images of Human Blastocysts","volume":"62","author":"Singh","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2968","DOI":"10.1109\/TBME.2017.2759665","article-title":"Automatic Identification of Human Blastocyst Components via Texture","volume":"64","author":"Saeedi","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.1093\/humrep\/des219","article-title":"A Method for Semi-Automatic Grading of Human Blastocyst Microscope Images","volume":"27","author":"Filho","year":"2012","journal-title":"Hum. Reprod."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kheradmand, S., Saeedi, P., and Bajic, I. (2016, January 15\u201319). Human Blastocyst Segmentation Using Neural Network. Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, Vancouver, BC, Canada.","DOI":"10.1109\/CCECE.2016.7726763"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kheradmand, S., Singh, A., Saeedi, P., Au, J., and Havelock, J. (2017, January 17\u201320). Inner Cell Mass Segmentation in Human HMC Embryo Images Using Fully Convolutional Network. Proceedings of the IEEE International Conference on Image Processing, Beijing, China.","DOI":"10.1109\/ICIP.2017.8296582"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rad, R.M., Saeedi, P., Au, J., and Havelock, J. (2018, January 7\u201310). Multi-Resolutional Ensemble of Stacked Dilated U-Net for Inner Cell Mass Segmentation in Human Embryonic Images. Proceedings of the 25th IEEE International Conference on Image Processing, Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451750"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rad, R.M., Saeedi, P., Au, J., and Havelock, J. (2019, January 22\u201325). BLAST-NET: Semantic Segmentation of Human Blastocyst Components via Cascaded Atrous Pyramid and Dense Progressive Upsampling. Proceedings of the IEEE International Conference on Image Processing, Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803139"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.imu.2018.10.009","article-title":"Human Blastocyst\u2019s Zona Pellucida Segmentation via Boosting Ensemble of Complementary Learning","volume":"13","author":"Rad","year":"2018","journal-title":"Inform. Med. Unlocked"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1016\/j.rbmo.2021.02.015","article-title":"Deep Learning Neural Network Analysis of Human Blastocyst Expansion from Time-Lapse Image Files","volume":"42","author":"Huang","year":"2021","journal-title":"Reprod. Biomed. Online"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101612","DOI":"10.1016\/j.media.2019.101612","article-title":"Trophectoderm Segmentation in Human Embryo Images via Inceptioned U-Net","volume":"62","author":"Rad","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"18927","DOI":"10.1109\/ACCESS.2021.3053098","article-title":"A Deep Learning Framework Design for Automatic Blastocyst Evaluation With Multifocal Images","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"205","DOI":"10.5455\/aim.2019.27.205-211","article-title":"A Review of Machine Learning Approaches in Assisted Reproductive Technologies","volume":"27","author":"Raef","year":"2019","journal-title":"Acta Inf. Med."},{"key":"ref_30","first-page":"1","article-title":"Artificial Intelligence as an Ally to Human Reproduction and Embryology","volume":"25","author":"Gomes","year":"2021","journal-title":"JBRA Assist. Reprod."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e372","DOI":"10.1016\/j.fertnstert.2018.07.1039","article-title":"Application of Artificial Intelligence Technology to Increase the Efficacy of Embryo Selection and Prediction of Live Birth Using Human Blastocysts Cultured in a Time-Lapse Incubator","volume":"110","author":"Zaninovic","year":"2018","journal-title":"Fertil. Steril."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Owais, M., Mahmood, T., Cho, S.W., and Park, K.R. (2019). Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation. J. Clin. Med., 8.","DOI":"10.3390\/jcm8091446"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Owais, M., Mahmood, T., Choi, J., and Park, K.R. (2020). Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases. J. Clin. Med., 9.","DOI":"10.3390\/jcm9030871"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105341","DOI":"10.1016\/j.cmpb.2020.105341","article-title":"Offline Computer-Aided Diagnosis for Glaucoma Detection Using Fundus Images Targeted at Mobile Devices","volume":"192","author":"Martins","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_35","unstructured":"(2018, May 16). SSS-Net. Available online: http:\/\/dm.dgu.edu\/link.html."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_37","unstructured":"Iglovikov, V., and Shvets, A. (2018). TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_39","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3534","DOI":"10.1109\/TIP.2019.2962688","article-title":"A Multistage Refinement Network for Salient Object Detection","volume":"29","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Salehi, S.S.M., Erdogmus, D., and Gholipour, A. (2017, January 10). Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. Proceedings of the Machine Learning in Medical Imaging, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-67389-9_44"},{"key":"ref_44","unstructured":"(2021, December 03). NVIDIA GeForce RTX 3080 Family. Available online: https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/30-series\/rtx-3080-3080ti\/."},{"key":"ref_45","unstructured":"(2021, December 03). MathWorks Introduces Release 2021a of MATLAB and Simulink. Available online: https:\/\/ch.mathworks.com\/company\/newsroom\/mathworks-introduces-release-2021a-of-matlab-and-simulink.html."},{"key":"ref_46","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization. Proceedings of the IEEE international Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/12\/2\/124\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:03:25Z","timestamp":1760133805000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/12\/2\/124"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,18]]},"references-count":47,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["jpm12020124"],"URL":"https:\/\/doi.org\/10.3390\/jpm12020124","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,18]]}}}