Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation
This work addresses segmentation accuracy and morphological feature preservation in biomedical images, but it is incremental as it builds on existing CNN methods.
The paper tackles biomedical image segmentation by using convolutional neural networks to iteratively grow predicted mask regions, achieving higher accuracy than a fully convolutional semantic segmentation CNN on retinal blood vessel images from the DRIVE database.
In this paper we present a methodology that uses convolutional neural networks (CNNs) for segmentation by iteratively growing predicted mask regions in each coordinate direction. The CNN is used to predict class probability scores in a small neighborhood of the center pixel in a tile of an image. We use a threshold on the CNN probability scores to determine whether pixels are added to the region and the iteration continues until no new pixels are added to the region. Our method is able to achieve high segmentation accuracy and preserve biologically realistic morphological features while leveraging small amounts of training data and maintaining computational efficiency. Using retinal blood vessel images from the DRIVE database we found that our method is more accurate than a fully convolutional semantic segmentation CNN for several evaluation metrics.