Experimenting with Deep Neural Networks for X-Ray Image Segmentation
Deep neural networks present a great interest for the field of medical image segmentation. This article shares the results of the exploratory phase of the research aimed at examining the potential of deep learning methods and encoder-decoder convolutional neural networks for lung image segmentation. The study was conducted by our partners at the Biomedical Image Analysis Department of the United Institute of Informatics Problems, National Academy of Sciences of Belarus.
Training data set
The training data set consisted of 354 chest X-ray images accompanied by the lung masks obtained through manual segmentation. Two different image sources were used:
- 107 images from the Belarus tuberculosis portal manually segmented during the preliminary phase of this project
- 247 images from the JSRT database
Examples of the original images and corresponding lung masks are illustrated in the following figure.
Network architecture and training parameters
In the figure below, you can find the neural network architecture that was used during the study.
The network had a typical deep architecture with the following key elements:
- 26 convolutional layers
- 25 batch normalization layers
- 25 ReLU layers
- 5 upsampling layers
All experiments and testing were performed using the Caffe framework. The input and output network fragments are illustrated in the figure below.
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