RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

By Qiangguo Jin, Zhao-Peng Meng, Changming Sun, Leyi Wei, Ran Su. Frontiers in Bioengineering and Biotechnology, 2018

439 citations recorded by the source corpus.

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, few studies investigate 3D networks for liver tumor segmentation. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver. The proposed network has a basic architecture as U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention residual modules are integrated so that the attention-aware features change adaptively. This is the first work that an attention residual mechanism is used to segment tumors from 3D medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and tested the generalization on the 3DIRCADb dataset. The experiments show that our architecture obtains competitive results.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5090fbfc9cf5db61ed060e5afdf01d2c08a8fcce

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation (2020, methodology): “Reference [35] proposed 3D RA-UNet which achieved dice scores 96.1% for liver segmentation and 59.5% for tumor segmentation.”
Medical Image Segmentation Review: The Success of U-Net (2022, methodology): “SCE Attention U-Net [70] UNet++ [11], [67] RA-Unet [71] BCDU-Net [74] U-Net3+ [68] BiO-Net [69] Skip connections are defined as connections in a neural network that do not connect two following layers but instead skip over at least one layer.”
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation (2021, methodology): “Existing medical image segmentation methods mainly rely on fully convolutional neural network (FCNN) with U-shaped structure [3,4,5].”
Transformers in Medical Imaging: A Survey (2022, background): “Further, we find it important to clarify that several alternate attention approaches [90]–[93] have been explored in the literature based on convolutional architectures.”