Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

Accepted to Medical Imaging meets NeurIPS workshop at NeurIPS 2019

Automated medical image segmentation plays a key role in quantitative research and diagnostics. Convolutional neural networks based on the U-Net architecture are the state-of-the-art. A key disadvantage is the hard-coding of the receptive field size, which requires architecture optimization for each segmentation task. Furthermore, increasing the receptive field results in an increasing number of weights. Recently, Neural Ordinary Differential Equations (NODE) have been proposed, a new type of continuous depth deep neural network. This framework allows for a dynamic receptive field at a constant memory cost and a smaller amount of parameters. We show on a colon gland segmentation dataset (GlaS) that these NODEs can be used within the U-Net framework to improve segmentation results while reducing memory load and parameter counts.

This reposity contains the code to reproduce the experiments from our paper.

Tip: start with the notebook

ArXiv link

Google Colab notebook (please note: training is quite slow on Google Colab.)

Requirements

pytorch
torchvision
numpy
cv2
PIL
matplotlib
scikit-image
scipy
torchdiffeq

Models

Schematic overview of U-Net and the proposed U-Node network. The U-ResNet architecture is equivalent to U-Node except for the ODE blocks.

Results

Example patches of the test set with clear differences between the models. Each color denotes a gland. The black bar denotes the receptive field. NFE denotes the number of function evaluations in the network. Difficult images (first and last row) seem to require more evaluations.

License

DIAGNijmegen/neural-odes-segmentation

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