U-Net##: A Powerful Novel Architecture for Medical Image Segmentation

Official PyTorch implementation of the MICAD 2022 conference paper: "U-Net##: A Powerful Novel Architecture for Medical Image Segmentation"

Information

In this paper, we propose a powerful novel architecture named U-Net##, which consists of multiple overlapping U-Net pathways and has the strategies of sharing feature maps between parallel neural networks, using auxiliary convolutional blocks for additional feature extractions and deep supervision, so that it performs as a boosted U-Net model for medical image segmentation.

UPDATE: New U-Net## Architecture

Changes:

• Feature map additions from distant network pathways are omitted.
• BatchNorm layers are changed to InstanceNorm layers.
• Number of filters is reduced by 50%.

Results:

• Better performance.
• ~4x faster training speed.
• Reduced parameters from 43M to 8.9M

New Architecture

Block Diagram of the New U-Net## Architecture

Convolutional Blocks of the New U-Net## Architecture

Original Architecture

Block Diagram of the Original U-Net## Architecture

Convolutional Blocks of the Original U-Net## Architecture

Results from the Original Architecture

The U-Net## model is evaluated on the TCIA-LGG Segmentation Dataset from The Cancer Imaging Archive (TCIA) to segment the brain regions with FLAIR abnormalities on the related brain MRI images.

Some Output Images Predicted by the Trained Models

Score Results of the Trained Models

Comparison of the Dice Score Changes

Citation

If you find this work useful for your research, please consider citing:

@InProceedings{10.1007/978-981-16-6775-6_19,
author={Korkmaz, Fırat},
editor={Su, Ruidan and Zhang, Yudong and Liu, Han and F Frangi, Alejandro},
title={U-Net##: A Powerful Novel Architecture for Medical Image Segmentation},
booktitle={Medical Imaging and Computer-Aided Diagnosis},
year={2023},
publisher={Springer Nature Singapore},
address={Singapore},
pages={231--241},
isbn={978-981-16-6775-6}
}