Synthetic Spermatozoa Video Sequences Generation Using Adversarial Imitation Learning

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dc.contributor.authorHernández-García, Sergio
dc.contributor.authorCuesta-Infante, Alfredo
dc.contributor.authorMontemayor, Antonio S.
dc.date.accessioned2025-01-30T12:30:48Z
dc.date.available2025-01-30T12:30:48Z
dc.date.issued2023-06-25
dc.descriptionThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-36616-1_45.
dc.description.abstractAutomated sperm sample analysis using computer vision techniques has gained increasing interest due to the tedious and time-consuming nature of manual evaluation. Deep learning models have been applied for sperm detection, tracking, motility analysis, and morphology recognition. However, the lack of labeled data hinders their adoption in laboratories. In this work, we propose a method to generate synthetic spermatozoa video sequences using Generative Adversarial Imitation Learning (GAIL). Our approach uses a parametric model based on Bezier splines to generate frames of a single spermatozoon. We evaluate our method against U-net and GAN-based approaches, and demonstrate its superior performance.
dc.identifier.citationHernández-García, S., Cuesta-Infante, A., Montemayor, A.S. (2023). Synthetic Spermatozoa Video Sequences Generation Using Adversarial Imitation Learning. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_45
dc.identifier.doi10.1007/978-3-031-36616-1_45
dc.identifier.isbn978-3-031-36616-1
dc.identifier.urihttps://hdl.handle.net/10115/71478
dc.language.isoen
dc.publisherSpringer
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectSynthetic data
dc.subjectImitation Learning
dc.subjectSperm analysis
dc.subjectComputer vision techniques
dc.subjectDeep learning
dc.subjectLabeled data
dc.subjectLearning models
dc.subjectMotility analysis
dc.subjectSample analysis
dc.subjectSequence generation
dc.subjectVideo recording
dc.subjectVideo sequences
dc.titleSynthetic Spermatozoa Video Sequences Generation Using Adversarial Imitation Learning
dc.typeBook chapter

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Automated sperm sample analysis using computer vision techniques has gained increasing interest due to the tedious and time-consuming nature of manual evaluation. Deep learning models have been applied for sperm detection, tracking, motility analysis, and morphology recognition. However, the lack of labeled data hinders their adoption in laboratories. In this work, we propose a method to generate synthetic spermatozoa video sequences using Generative Adversarial Imitation Learning (GAIL). Our approach uses a parametric model based on Bezier splines to generate frames of a single spermatozoon. We evaluate our method against U-net and GAN-based approaches, and demonstrate its superior performance.
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