Liver segmengtation using deep learning

we use 3DResUnet to segment liver in CT images and use DenseCRF as post processing. I write as much comment as possible and hope you will find this repo useful!

Dataset

Liver tumor Segmentation Challenge (LiTS) contain 131 contrast-enhanced CT images provided by hospital around the world. 3DIRCADb dataset is a subset of LiTS dataset with case number from 27 to 48. we train our model with 111 cases from LiTS after removeing the data from 3DIRCADb and evaluate on 3DIRCADb dataset. For more detail about the dataset, you can check this link: https://competitions.codalab.org/competitions/17094

Experiment

The whole traning process run on three GTX-1080Ti with batch size epual to three, we show some of the segmentation resluts of our 3DResUNet evaluate on 3DIRCADb dataset. Data augmentation is not used during the training process, because it is observed in the experiment that data enhancement such as random rotation or elastic deformation will lead to decrease in accuracy.

The loss curve is shown below which is draw by visdom.

Usage

I write all the parameter in parameter.py, so first set dataset path etc of your own and then run ./data_pareper/get_training_set.py to get the training set, then you can run ./train_ds.py to train the the network from scratch. after the model is well trained, run val.py to test the model on test set, if you want to use DenseCRF as post processing, run ./Densecrf/3D-CRF.py if you get enough memory, or run ./Densecrf/2D-CRF.py other wise.

Main references:

1. Milletari F, Navab N, Ahmadi S A. V-net: Fully convolutional neural networks for volumetric medical image segmentation[C]//2016 Fourth International Conference on 3D Vision (3DV). IEEE, 2016: 565-571.
2. Wong K C L, Moradi M, Tang H, et al. 3d segmentation with exponential logarithmic loss for highly unbalanced object sizes[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018: 612-619.
3. Yuan Y, Chao M, Lo Y C. Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance[J]. IEEE transactions on medical imaging, 2017, 36(9): 1876-1886.
4. Salehi S S M, Erdogmus D, Gholipour A. Tversky loss function for image segmentation using 3D fully convolutional deep networks[C]//International Workshop on Machine Learning in Medical Imaging. Springer, Cham, 2017: 379-387.
5. Brosch T, Yoo Y, Tang L Y W, et al. Deep convolutional encoder networks for multiple sclerosis lesion segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2015: 3-11.
6. Xu W, Liu H, Wang X, et al. Liver Segmentation in CT based on ResUNet with 3D Probabilistic and Geometric Post Process[C]//2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP). IEEE, 2019: 685-689.
7. Krähenbühl P, Koltun V. Efficient inference in fully connected crfs with gaussian edge potentials[C]//Advances in neural information processing systems. 2011: 109-117.