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short-paper

VISEM: a multimodal video dataset of human spermatozoa

Authors:

Trine B. Haugen,

Steven A. Hicks,

Jorunn M. Andersen,

Oliwia Witczak,

Hugo L. Hammer,

Pål Halvorsen,

Michael RieglerAuthors Info & Claims

MMSys '19: Proceedings of the 10th ACM Multimedia Systems Conference

Pages 261 - 266

https://doi.org/10.1145/3304109.3325814

Published: 18 June 2019 Publication History

Abstract

Real multimedia datasets that contain more than just images or text are rare. Even more so are open multimedia datasets in medicine. Often, clinically related datasets only consist of image or videos. In this paper, we present a dataset that is novel in two ways. Firstly, it is a multi-modal dataset containing different data sources such as videos, biological analysis data, and participant data. Secondly, it is the first dataset of that kind in the field of human reproduction. It consists of anonymized data from 85 different participants. We hope this dataset paper will inspire people to apply their knowledge in this important field, generate shareable results in the domain, and ultimately improve human infertility investigation and treatment.

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Cited By

Sadak FGerena EDupont CLévy RHaliyo S(2024)Human Sperm Detection and Tracking using Event-based Cameras and Unsupervised Learning2024 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)10.1109/MARSS61851.2024.10612710(1-6)Online publication date: 1-Jul-2024
https://doi.org/10.1109/MARSS61851.2024.10612710
Maier-Hein LReinke AGodau PTizabi MBuettner FChristodoulou EGlocker BIsensee FKleesiek JKozubek MReyes MRiegler MWiesenfarth MKavur ASudre CBaumgartner MEisenmann MHeckmann-Nötzel DRädsch TAcion LAntonelli MArbel TBakas SBenis ABlaschko MCardoso MCheplygina VCimini BCollins GFarahani KFerrer LGaldran Avan Ginneken BHaase RHashimoto DHoffman MHuisman MJannin PKahn CKainmueller DKainz BKarargyris AKarthikesalingam AKofler FKopp-Schneider AKreshuk AKurc TLandman BLitjens GMadani AMaier-Hein KMartel AMattson PMeijering EMenze BMoons KMüller HNichyporuk BNickel FPetersen JRajpoot NRieke NSaez-Rodriguez JSánchez CShetty Svan Smeden MSummers RTaha ATiulpin ATsaftaris SVan Calster BVaroquaux GJäger P(2024)Metrics reloaded: recommendations for image analysis validationNature Methods10.1038/s41592-023-02151-z21:2(195-212)Online publication date: 12-Feb-2024
https://doi.org/10.1038/s41592-023-02151-z
Adinugroho SNakazawa A(2024)Deep learning-based sperm motility and morphology estimation on stacked color-coded MotionFlowInformatics in Medicine Unlocked10.1016/j.imu.2024.10145945(101459)Online publication date: 2024
https://doi.org/10.1016/j.imu.2024.101459
Show More Cited By

Index Terms

VISEM: a multimodal video dataset of human spermatozoa
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Visual inspection
  2. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees
      2. Neural networks

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Information & Contributors

Information

Published In

cover image ACM Conferences

MMSys '19: Proceedings of the 10th ACM Multimedia Systems Conference

June 2019

374 pages

ISBN:9781450362979

DOI:10.1145/3304109

General Chair:
Michael Zink
University of Massachusetts Amherst
,
Program Chairs:
Laura Toni
University College London
,
Ali C. Begen
Özyeğin University/Networked Media

Copyright © 2019 Owner/Author.

This work is licensed under a Creative Commons Attribution-NoDerivs International 4.0 License.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2019

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Norwegian Research Council

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MMSys '19

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SIGMM

MMSys '19: 10th ACM Multimedia Systems Conference

June 18 - 21, 2019

Massachusetts, Amherst

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MMSys '19 Paper Acceptance Rate 40 of 82 submissions, 49%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Sadak FGerena EDupont CLévy RHaliyo S(2024)Human Sperm Detection and Tracking using Event-based Cameras and Unsupervised Learning2024 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)10.1109/MARSS61851.2024.10612710(1-6)Online publication date: 1-Jul-2024
https://doi.org/10.1109/MARSS61851.2024.10612710
Maier-Hein LReinke AGodau PTizabi MBuettner FChristodoulou EGlocker BIsensee FKleesiek JKozubek MReyes MRiegler MWiesenfarth MKavur ASudre CBaumgartner MEisenmann MHeckmann-Nötzel DRädsch TAcion LAntonelli MArbel TBakas SBenis ABlaschko MCardoso MCheplygina VCimini BCollins GFarahani KFerrer LGaldran Avan Ginneken BHaase RHashimoto DHoffman MHuisman MJannin PKahn CKainmueller DKainz BKarargyris AKarthikesalingam AKofler FKopp-Schneider AKreshuk AKurc TLandman BLitjens GMadani AMaier-Hein KMartel AMattson PMeijering EMenze BMoons KMüller HNichyporuk BNickel FPetersen JRajpoot NRieke NSaez-Rodriguez JSánchez CShetty Svan Smeden MSummers RTaha ATiulpin ATsaftaris SVan Calster BVaroquaux GJäger P(2024)Metrics reloaded: recommendations for image analysis validationNature Methods10.1038/s41592-023-02151-z21:2(195-212)Online publication date: 12-Feb-2024
https://doi.org/10.1038/s41592-023-02151-z
Adinugroho SNakazawa A(2024)Deep learning-based sperm motility and morphology estimation on stacked color-coded MotionFlowInformatics in Medicine Unlocked10.1016/j.imu.2024.10145945(101459)Online publication date: 2024
https://doi.org/10.1016/j.imu.2024.101459
Dobrovolny MBenes JLanger JKrejcar OSelamat A(2023)Study on Sperm-Cell Detection Using YOLOv5 Architecture with Labaled DatasetGenes10.3390/genes1402045114:2(451)Online publication date: 9-Feb-2023
https://doi.org/10.3390/genes14020451
Zhu RCui YHuang JHou EZhao JZhou ZLi H(2023)YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm DetectionDiagnostics10.3390/diagnostics1306110013:6(1100)Online publication date: 14-Mar-2023
https://doi.org/10.3390/diagnostics13061100
Nguyen VHa Ngo TDuong LTran M(2023)Deep Learning-based Automated Assessment of Sperm Motility2023 1st International Conference on Health Science and Technology (ICHST)10.1109/ICHST59286.2023.10565344(1-6)Online publication date: 28-Dec-2023
https://doi.org/10.1109/ICHST59286.2023.10565344
Alonso AKirkegaard J(2023)Fast detection of slender bodies in high density microscopy dataCommunications Biology10.1038/s42003-023-05098-16:1Online publication date: 19-Jul-2023
https://doi.org/10.1038/s42003-023-05098-1
Thambawita VHicks SStorås ANguyen TAndersen JWitczak OHaugen THammer HHalvorsen PRiegler M(2023)VISEM-Tracking, a human spermatozoa tracking datasetScientific Data10.1038/s41597-023-02173-410:1Online publication date: 9-May-2023
https://doi.org/10.1038/s41597-023-02173-4
Yuzkat MIlhan HAydin N(2023)Detection of sperm cells by single-stage and two-stage deep object detectorsBiomedical Signal Processing and Control10.1016/j.bspc.2023.10463083(104630)Online publication date: May-2023
https://doi.org/10.1016/j.bspc.2023.104630
Barkavi RYamuna GJayaram C(2023)Artificial Intelligence: Revolution in Assisted Reproductive TechnologyProceedings of International Conference on Communication and Computational Technologies10.1007/978-981-99-3485-0_76(947-961)Online publication date: 1-Sep-2023
https://doi.org/10.1007/978-981-99-3485-0_76
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