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Automated Measurement of Ovary Development in Atlantic Salmon Using Deep Learning.

Yasin Yari1, Ingun Næve2, Asle Hammerdal3

  • 1Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.

Ultrasound in Medicine & Biology
|January 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel AI model for real-time ultrasound image segmentation of Atlantic salmon ovaries, improving maturation state monitoring. The developed system achieves efficient segmentation, enabling automated smart monitoring for salmon breeding.

Keywords:
Atlantic salmon ovary segmentationMaturation predictionSegmentationUltrasound and deep learning

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Area of Science:

  • Aquaculture technology
  • Biomedical imaging
  • Artificial intelligence in agriculture

Background:

  • Accurate monitoring of salmon maturation is crucial for breeding success.
  • Current ultrasound methods for salmon ovary assessment are subjective and labor-intensive.
  • Real-time ultrasound image segmentation offers a potential solution to improve objectivity and efficiency.

Purpose of the Study:

  • To develop an automated system for real-time ultrasound image segmentation of Atlantic salmon ovaries.
  • To address challenges in segmentation accuracy, including false predictions and shape inconsistencies.
  • To enable precise monitoring of salmon maturation states.

Main Methods:

  • A modified U-Net model was employed for segmentation and classification tasks.
  • Variational autoencoder (VAE) and generative adversarial network (GAN) were integrated to refine ovary shape predictions.
  • A dataset of Atlantic salmon ovaries across two maturation periods was utilized for model training and validation.

Main Results:

  • The proposed model achieved an average Dice score of 0.885 for real-time salmon ovary segmentation.
  • Performance was validated against conventional and novel U-Net architectures.
  • The method demonstrated effectiveness in on-site salmon ultrasound examination settings.

Conclusions:

  • The developed AI approach provides a competitive and efficient solution for salmon ovary segmentation.
  • This technology enables the design of automated systems for smart monitoring of maturation in Atlantic salmon.
  • The findings support the advancement of precision aquaculture through AI-driven imaging analysis.