PHiSeg Code

Public tensorflow implementation for our paper PHiSeg: Capturing Uncertainty in Medical Image Segmentation method, which was accepted for presentation at MICCAI 2019.

If you find this code helpful in your research please cite the following paper:

@article{PHiSeg2019Baumgartner,
         author={Baumgartner, Christian F. and Tezcan, Kerem C. and
         Chaitanya, Krishna and H{\"o}tker, Andreas M. and
         Muehlematter, Urs J. and Schawkat, Khoschy and Becker, Anton S. and
         Donati, Olivio and Konukoglu, Ender},
         title={{PHiSeg}: Capturing Uncertainty in Medical Image Segmentation},
         journal={arXiv:1906.04045},
         year={2019},
}

Method overview

Many medical image segmentation tasks are inherently ambiguous. For example, if six radiologists are asked to segment two regions of the prostate in an MR image, you will get six different answers:

We address this problem by developing a hierarchical probabilistic model that - unlike most conventional segmentation techniques - does not produce a single segmentation, but rather produces samples from the distribution of probable segmentations for a specific image.

Here is an example of our methods output for the same test image.

It can be seen that the samples are very similar to those generated by the 6 experts above.

Having access to such samples allows us to give a human user of this tool several options to choose from. The model can also be used to determine the most likely sample according to our model, and to visualize areas of high uncertainty.

The method functions by constructing a hierarchical probabilistic model that assumes a generative process for the segmentation s, given the image x, in which the image is constructed one resolution level at a time (similar to Laplacian pyramids). The generation of each resolution level is assumed to be governed by a hidden, low dimensional variable z_l. Here is an image of the graphical model (right) along with and example of the generative process (left).

In our paper, we show that inference in this probabilistic model can be performed using a variation of the well known autoencoding variational Bayes framework.

In the paper, we show that we outperform the related probabilistic U-NET on two relevant metrics. We furthermore show that taking the probabilistic perspective for the segmentation problem comes at no cost for segmentation accuracy! Our method performs just as well (in fact slightly better) than a deterministic U-NET on the conventional Dice Score metric.

Virtual Environment Setup

The code is implemented in Python 3.5 using using the tensorflow library. We only tested the code with tensorflow 1.12. One way of getting all the requirements is using virtualenv and the requirements.txt file.

• Set up a virtual environment (e.g. conda or virtualenv) with Python 3.5
• Install all non-tensorflow requirements using:

pip install -r requirements.txt

• Install the GPU version of tensorflow using

pip install -r tensorflow-gpu==1.12

Running the code

Before running the code, a number of parameters must be configured:

• Open config/system.py and change the settings to match your system.

• Download the LIDC data from Stefan Knegt's gihub page.

• In the experiment files under phiseg/experiments adapt the paths for the source data and the target path for the preprocessd data.

Then the code can be run using the following command:

• Start training a model by running phiseg_train.py with the corresponding experiment file. For example

python phiseg_train.py phiseg/experiments/phiseg_7_5.py

• The easiest way to monitor training is using tensorboard.

• After training has finished, you can use phiseg_generate_samples.py to sample from the learned distribution, and phiseg_test_quantitative to obtain the quantitative results reported in the paper.

Data

The public implementation of our code currently trains and evaluates on the publicly available LIDC Chest Lesion dataset. We used the preprocessed data available on Stefan Knegt's gihub page (see link at the very bottom).

The prostate dataset collected at University Hospital Zurich is unfortuantely not publicly available as of yet.

Code structure

Under phiseg/experiments you will find a number of experiment config files, where the architecture and training details of the method are specified. Modify these files to explore different settings.

Using the code with your own data

If you want to use your own data, you need to create an appropriate data loader (see data/lid_data_loader.py) and a data provider (see data/lidc_data.py). You also need to add the dataset to data/data_switch.py.