AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation

TL;DR

This paper introduces AWEU-Net, an attention-aware deep learning model that enhances lung nodule segmentation accuracy in CT images, aiding early lung cancer diagnosis with state-of-the-art performance.

Contribution

The paper presents a novel attention-aware weight excitation U-Net (AWEU-Net) with position and channel attention modules for improved lung nodule segmentation.

Findings

Achieved Dice scores of 89.79% and 90.35% on LUNA16 and LIDC-IDRI datasets.

Obtained IoU scores of 82.34% and 83.21% on the same datasets.

Demonstrated superior performance over existing segmentation methods.

Abstract

Lung cancer is deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is the most important part of diagnosing lung cancer in the early stage. Most of the existing systems are semi-automated and need to manually select the lung and nodules regions to perform the segmentation task. To address these challenges, we proposed a fully automated end-to-end lung nodule detection and segmentation system based on a deep learning approach. In this paper, we used Optimized Faster R-CNN; a state-of-the-art detection model to detect the lung nodule regions in the CT scans. Furthermore, we proposed an attention-aware weight excitation U-Net, called AWEU-Net, for lung nodule segmentation and boundaries detection. To achieve more accurate nodule segmentation, in AWEU-Net, we proposed position attention-aware weight excitation (PAWE), and channel attention-aware weight excitation (CAWE) blocks to highlight the best aligned spatial and channel features in the input feature maps. The experimental results demonstrate that our proposed model yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly LUNA16 and LIDC-IDRI datasets, respectively.