UNet++: A Nested U-Net Architecture for Medical Image Segmentation
This work addresses segmentation accuracy for medical imaging applications, representing an incremental improvement over existing U-Net variants.
The paper tackles medical image segmentation by introducing UNet++, a nested U-Net architecture with dense skip pathways to reduce semantic gaps, achieving average IoU gains of 3.9 and 3.4 points over U-Net and wide U-Net across multiple tasks.
In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar. We have evaluated UNet++ in comparison with U-Net and wide U-Net architectures across multiple medical image segmentation tasks: nodule segmentation in the low-dose CT scans of chest, nuclei segmentation in the microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos. Our experiments demonstrate that UNet++ with deep supervision achieves an average IoU gain of 3.9 and 3.4 points over U-Net and wide U-Net, respectively.