U-Net++

UNet++ is an architecture for semantic segmentation based on the U-Net. Through the use of densely connected nested decoder sub-networks, it enhances extracted feature processing and was reported by its authors to outperform the U-Net in Electron Microscopy (EM), Cell, Nuclei, Brain Tumor, Liver and Lung Nodule medical image segmentation tasks.

Key Features

• Nested Dense Skip Pathways: Redesigned skip connections reduce the semantic gap between encoder and decoder feature maps, enabling the optimizer to learn more efficiently.
• Deep Supervision: Multi-level supervision during training improves convergence and segmentation accuracy.

Reference

• Paper: UNet++: A Nested U-Net Architecture for Medical Image Segmentation
• Authors: Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang
• Published: Accepted by the 4th Deep Learning in Medical Image Analysis (DLMIA) Workshop.

Citation

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