EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation

08/19/2022
by   Shiqiang Ma, et al.
16

Automatic image segmentation technology is critical to the visual analysis. The autoencoder architecture has satisfying performance in various image segmentation tasks. However, autoencoders based on convolutional neural networks (CNN) seem to encounter a bottleneck in improving the accuracy of semantic segmentation. Increasing the inter-class distance between foreground and background is an inherent characteristic of the segmentation network. However, segmentation networks pay too much attention to the main visual difference between foreground and background, and ignores the detailed edge information, which leads to a reduction in the accuracy of edge segmentation. In this paper, we propose a light-weight end-to-end segmentation framework based on multi-task learning, termed Edge Attention autoencoder Network (EAA-Net), to improve edge segmentation ability. Our approach not only utilizes the segmentation network to obtain inter-class features, but also applies the reconstruction network to extract intra-class features among the foregrounds. We further design a intra-class and inter-class features fusion module – I2 fusion module. The I2 fusion module is used to merge intra-class and inter-class features, and use a soft attention mechanism to remove invalid background information. Experimental results show that our method performs well in medical image segmentation tasks. EAA-Net is easy to implement and has small calculation cost.

READ FULL TEXT

page 2

page 3

page 5

page 10

research
05/03/2020

Boundary-aware Context Neural Network for Medical Image Segmentation

Medical image segmentation can provide a reliable basis for further clin...
research
05/21/2020

Unsupervised segmentation via semantic-apparent feature fusion

Foreground segmentation is an essential task in the field of image under...
research
07/21/2023

Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation

Medical image segmentation is a challenging task, particularly due to in...
research
07/29/2020

Learning To Pay Attention To Mistakes

As evidenced in visual results in <cit.><cit.><cit.><cit.><cit.>, the pe...
research
06/28/2023

Chan-Vese Attention U-Net: An attention mechanism for robust segmentation

When studying the results of a segmentation algorithm using convolutiona...
research
04/05/2018

Multi-level Activation for Segmentation of Hierarchically-nested Classes

For a number of biomedical image segmentation tasks, including topologic...
research
08/10/2016

Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation

This study introduced a novel system, called Gaze2Segment, integrating b...

Please sign up or login with your details

Forgot password? Click here to reset