Object recognition in the ovary: quantification of oocytes from microscopic images

  • Authors:

  • Angelos Skodras;Stamatia Giannarou;Mark Fenwick;Stephen Franks;Jaroslav Stark;Kate Hardy

  • Affiliations:

  • Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Hospital, London, United Kingdom;Institute of Biomedical Engineering, Imperial College London, London, United Kingdom;Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Hospital, London, United Kingdom;Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Hospital, London, United Kingdom;Department of Mathematics and Centre for Integrative Systems Biology at Imperial College, Imperial College London, London, United Kingdom;Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Hospital, London, United Kingdom

  • Venue:
  • DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
  • Year:
  • 2009

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Abstract

The ovary is a female organ that houses a fixed supply of germ cells (oocytes). The absolute number of oocytes at any given stage can be a useful indicator of fertility. Obtaining accurate assessments of the oocyte reserve in humans and experimental models can be time consuming and error prone. In this paper a new approach to facilitate oocyte counting in microscope images of mouse ovaries is presented. The mouse vasa homolog (MVH), an oocyte-specific protein, was labeled in microscope sections and used to develop an algorithm that can identify, count and estimate the size and coordinates of the oocytes. We use this automated approach to generate comparable data with conventional methods of oocyte counting.