Attention Augmented ConvNeXt UNet For Rectal Tumour Segmentation

TL;DR

This paper introduces AACN-UNet, a novel deep learning model combining ConvNeXt and CBAM attention mechanisms, significantly improving rectal tumor segmentation accuracy in CT images.

Contribution

It proposes a new architecture that integrates ConvNeXt encoder and CBAM attention to enhance feature extraction and segmentation performance in rectal tumor detection.

Findings

AACN-UNet outperforms existing models by 0.9-1.4% in P, F1, and Miou metrics.

The model achieves higher accuracy with fewer parameters and comparable training time.

It effectively extracts features from CT images, improving tumor segmentation results.

Abstract

It is a challenge to segment the location and size of rectal cancer tumours through deep learning. In this paper, in order to improve the ability of extracting suffi-cient feature information in rectal tumour segmentation, attention enlarged ConvNeXt UNet (AACN-UNet), is proposed. The network mainly includes two improvements: 1) the encoder stage of UNet is changed to ConvNeXt structure for encoding operation, which can not only integrate multi-scale semantic information on a large scale, but al-so reduce information loss and extract more feature information from CT images; 2) CBAM attention mechanism is added to improve the connection of each feature in channel and space, which is conducive to extracting the effective feature of the target and improving the segmentation accuracy.The experiment with UNet and its variant network shows that AACN-UNet is 0.9% ,1.1% and 1.4% higher than the current best results in P, F1 and Miou.Compared with the training time, the number of parameters in UNet network is less. This shows that our proposed AACN-UNet has achieved ex-cellent results in CT image segmentation of rectal cancer.