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EndoVis2018

MICCAI challenge for EndoVis2018. The challenge focuses on surgical scene segmentation.

Final Report

See pdf for more details.

Demo video

Result

Mini-Data (20% uniform subsampling)

Network 0 1 2 3 4 5 6 7 8 9 10 11 Mean
UNet 0.87 0.86 0.73 0.76 0.82 0.84 0.68 0.85 0.00 0.66 0.88 0.58 0.71
AlbuNet 0.92 0.91 0.80 0.79 0.90 0.90 0.68 0.78 0.00 0.76 0.91 0.71 0.76
AlbuNet+SuperLabel 0.93 0.93 0.82 0.80 0.91 0.90 0.62 0.86 0.00 0.78 0.92 0.77 0.77
DeepLabV3+ 0.91 0.93 0.81 0.82 0.94 0.87 0.51 0.60 0.00 0.76 0.92 0.73 0.73
DeepLabv3+SuperLabel 0.93 0.93 0.83 0.79 0.91 0.90 0.64 0.85 0.00 0.79 0.92 0.82 0.78
DeepLabV3+Aug 0.90 0.94 0.80 0.84 0.94 0.84 0.53 0.68 0.00 0.59 0.81 0.81 0.72
DeepLabv3+SuperLabel+Aug 0.94 0.93 0.83 0.81 0.92 0.92 0.64 0.84 0.00 0.81 0.94 0.83 0.78

Sequence sample

Network 0 1 2 3 4 5 6 7 8 9 10 11 Mean
UNet 0.66 0.87 0.76 0.77 0.41 0.22 0.35 0.22 0.00 0.09 0.53 0.00 0.41
AlbuNet 0.69 0.90 0.76 0.78 0.51 0.29 0.38 0.15 0.00 0.23 0.59 0.01 0.44
AlbuNet+SuperLabel 0.75 0.94 0.79 0.84 0.60 0.43 0.43 0.45 0.00 0.45 0.62 0.00 0.53
DeepLabV3+ 0.74 0.89 0.76 0.80 0.65 0.29 0.30 0.40 0.00 0.06 0.56 0.00 0.45
DeepLabv3+SuperLabel 0.74 0.92 0.78 0.83 0.64 0.33 0.33 0.39 0.00 0.20 0.59 0.00 0.48

Random sample

Network 0 1 2 3 4 5 6 7 8 9 10 11 Mean
AlbuNet+SuperLabel 0.96 0.96 0.9 0.88 0.96 0.95 0.72 0.9 0 0.83 0.96 0.87 0.82
DeepLabV3+ 0.96 0.95 0.89 0.87 0.96 0.96 0.69 0.9 0.37 0.84 0.97 0.86 0.85
DeepLabv3+SuperLabel 0.97 0.96 0.89 0.87 0.96 0.96 0.7 0.9 0.38 0.82 0.96 0.89 0.86

Visual comparison of with/without superlabel

see Comparison.ipynb

Instruction for Tensorboardx

pip install tensorboardX
pip install tensorflow

start tensorboard by tensorboard --logdir=<dir_to_store_log_file>

Example Notebook

  1. UNet
  2. AlbuNet
  3. DeepLabV3+
  4. AlbuNet SuperLabel
  5. DeepLabV3+ SuperLabel

Architecture

  1. UNet
  2. AlbuNet
  3. DeepLabV3+
  4. AlbuNet SuperLabel
  5. DeepLabV3+ SuperLabel

Components

  1. model_training.py
  2. dataset.py
  3. dice_loss.py
  4. etc.

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MICCAI challenge for EndoVis2018

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