khushhallchandra / LiverSegmentation

suggested solution for Liver segmentation TAU project

LiverSegmentation

suggested solution for Liver segmentation TAU project

training procedure:

• put the provided data in the main folder, under the folder data
• run data_preprocessing.py, this should create a file named liver_dataset.npz in you data folder
• run training.py, this should save trained net files. the results of my training session are saved under trained_model folder
• predict_test.py is an example script that predicts on a test folder

training output:

Using Theano backend.
WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10).  Please switch to the gpuarray backend. You can get more information about how to switch at this URL:
 https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29

Using gpu device 0: GeForce GTX 650 Ti (CNMeM is disabled, cuDNN 5110)
Train on 1092 samples, validate on 500 samples
Epoch 1/6
1092/1092 [==============================] - 305s - loss: 0.0612 - acc: 0.9731 - val_loss: 0.0215 - val_acc: 0.9924
Epoch 2/6
1092/1092 [==============================] - 305s - loss: 0.0193 - acc: 0.9917 - val_loss: 0.0189 - val_acc: 0.9937
Epoch 3/6
1092/1092 [==============================] - 306s - loss: 0.0135 - acc: 0.9939 - val_loss: 0.0225 - val_acc: 0.9941
Epoch 4/6
1092/1092 [==============================] - 306s - loss: 0.0086 - acc: 0.9967 - val_loss: 0.0237 - val_acc: 0.9934
Epoch 5/6
1092/1092 [==============================] - 306s - loss: 0.0079 - acc: 0.9969 - val_loss: 0.0194 - val_acc: 0.9940
Epoch 6/6
1092/1092 [==============================] - 306s - loss: 0.0063 - acc: 0.9975 - val_loss: 0.0221 - val_acc: 0.9937

liver segmentation stats:
dice coeff = 0.990807830811   ppv = 0.990807830811   sensitivity = 0.990807830811
cancer segmentation stats:
dice coeff = 0.969423287535   ppv = 0.940785339355   sensitivity = 0.999859480348