This is the implementation code for paper submitted to Bioinformatics: "DeepEM3D: Approaching human-level performance on 3D anisotropic EM image segmentation "

C++, bash shell, matlab, Cuda7.5

(1). Register at: http://brainiac2.mit.edu/SNEMI3D/user/register

(2). Login in and download data at: http://brainiac2.mit.edu/SNEMI3D/downloads

(3) Convert image files into h5 file that contains \data and \label sets.

1. To generate boundary labels:
   run matlab scripts: /scripts/create_new_vertical_closed_label.m

2. To generate all data h5 files (train, valid, test):
   run matlab scripts: /scripts/read_data_write_data_with_enhanced_labels.m

3. To train and predict netwroks models:
   run shell scripts: /model/inception_residual_train_prediction_xfm/train.sh or predict.sh

4. To generate segmentation on test set:
   run matlab scripts /model/inception_residual_train_prediction_3fm/run_segmentation_on_test_set.m