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+# Pancreas Segmentation in Abdominal CT Scans
+### Introduction
+---------
+This is the code repository for the abstract [Pancreas Segmentation in Abdominal CT Scans](Yijun_ISBI181page_final.pdf) presented at [IEEE International Symposium on Biomedical Imaging (ISBI) 2018](http://biomedicalimaging.org/2018/). The code for data preparation, test and utilities is largely from [OrganSegC2F](https://github.com/198808xc/OrganSegC2F). Please follow their requirements if you want to use the code in your work. There are no restrictions other than this.
+
+We propose a U-Net based approach for pancreas segmentation. Under the same setting where bounding boxes are provided, this method outperforms previously reported results with a mean Dice Coefficient of 86.70 for the NIH dataset with 4-fold cross validation. Results show that a network designed specifically for and trained from scratch with biomedical images can achieve a better performance with much less training time compared to fine-tuning the models that are designed for and pre-trained on natural images.
+
+### Main Dependencies
+----------
+- python (2.7)
+
+- tensorflow-gpu (1.3.0)
+
+- Keras (2.0.8)
+
+- numpy (1.13.1)
+
+- pandas (0.20.3)
+
+- matplotlib (used for test output visualization)
+
+### To run the experiment
+--------
+Step 1. Navigate to your project root directory, download the [pancreas segmentation dataset](https://wiki.cancerimagingarchive.net/display/Public/Pancreas-CT), use the [code](https://github.com/198808xc/OrganSegC2F/tree/master/DATA2NPY) to convert the images and annotations to numpy arrays.
+
+Step 2. Clone this repo in your project root directory.
+
+Step 3. Modify the path variables in `pipeline` to fit your own settings.
+
+Step 4. Execute script
+
+    chmod +x pipeline
+    ./pipeline
+
+Step 5. Modify the `cur_fold` variable in script `pipeline` to run in different fold. 
+
+After each round, there should be 
+
+1. A `test_stats.csv` in `/project-root-dir/data/test-records/` which records DSC mean and standard deviation for each fold
+2. A `/project-root-dir/data/test-records/{test_model_name}.csv` which records DSC for each test case
+3. Output prediction segmentation in `/project-root-dir/data/test-records/pred-{current_fold}` for each test case
+
+Note: since the code is not well tested after clean-up, there may be some caveats when running the code. Issues and PRs are welcome.
+
+### References
+-----------
+[1] Y. Zhou, L. Xie, W. Shen, Y. Wang, E. Fishman and A. Yuille, "A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans", Proc. MICCAI, 2017
+
+[2] H. Roth, L. Lu, A Farag, H-C Shin, J Liu, E. Turkbey, and R. M. Summers, "DeepOrgan: Multi-level deep convolutional networks for automated pancreas segmentation", Proc. MICCAI, 2015.
+
+[3] O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation", Proc. MICCAI, 2015.