3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes

Understand

With the introduction of fully convolutional neural networks, deep learning has raised the benchmark for medical image segmentation on both speed and accuracy, and different networks have been proposed for 2D and 3D segmentation with promising results.

• Nevertheless, most networks only handle relatively small numbers of labels (<10), and there are very limited works on handling highly unbalanced object sizes especially in 3D segmentation.
• In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures.
• By combining skip connections and deep supervision with respect to the computational feasibility of 3D segmentation, we propose a fast converging and computationally efficient network architecture for accurate segmentation.