Image Segmentation
Transformers
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3DL_NuCount model

Model author: Fabrice Daian

Model description

3DL_NuCount model has been designed by fine tuning a pretrained Stardist3D model [1,2] using a home made dataset [3] in order to assess the number of cells present in a given 3D image stack acquired using an optical microscope. Training and Inference Notebooks are hosted on our Github repo [4].

Stardist Training parameters

  • patch size: (48,96,96)
  • batch size: 32
  • epochs : 100
  • data augmentation : flip/rotation/intensity
  • image normalization: normalize channel independantly
  • anisotropy: empirical
  • rays : 96

Training dataset parameters

  • tile size : (4,63,128,128)
  • split : Train 0.8 / Val 0.2

Inference

  • patch size : (784,784,:)
  • image size : (2048,2048,:)
  • model name : weights_best_1.h5
  • config file : config.json
  • threshold file : thresholds.json

References

  • [1] Stardist Project: Github
  • [2] Stardist Paper : ArXiv
  • [3] NuCount Training Dataset : Zenodo
  • [4] NuCount Github Project: Github
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