Detnet: Deep Neural Network For Particle Detection In Fluorescence Microscopy Images

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What is this post about?

Short take

  • Hourglass-shaped network for detection of spots/foci/particles in cells
  • Based on Deconvolution network with the following changes
    • Reduced number of feature maps
    • Reduces size of receptive field (two instead of five poolings)
    • No long-range skip connections (detailed boundary information is irrelevant)
    • Use residual blocks instead of convolution (to reduce issue of vanishing gradient)
    • Use instance- instead of batch-normalisation (because little training data)
    • Use bilinear upsampling instead of transposed convolutions (expanding path)
    • 17k parameters instead of U-net’s 1.9M
    • Use soft Dice loss (performs implicit class balancing and penalizes easy samples)
    • Use early stopping
    • Availability
      • No information given, no links, not GitHub names
      • Contact with first-author points to company in Berlin


  • Details
    • AMSGrad optimizer
    • in-house hyperparameter optimization framework HyperHyper
  • Quotes
    • domain adapted Deconvolution Network
    • can be trained with only a few ground truth annotations
    • benchmarked using data from the ISBI Particle Tracking Challenge
    • including particles with different shapes (round and elongated)
    • when using a standard Cross-Entropy loss, training was not successful due to the heavy class imbalance
    • the F1 score can be significantly improved by optimizing the shift a ∈ R of the sigmoid function of the neural network
    • The assignment between particle detections and ground truth was determined by the Munkres algorithm with a maximal gating distance of 5 pixels.