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

• URL: http://arxiv.org/abs/2110.05144v1
• Date: Mon, 11 Oct 2021 10:35:44 GMT
• Title: AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
• Authors: Syeda Furruka Banu, Md. Mostafa Kamal Sarker, Mohamed Abdel-Nasser, Domenec Puig and Hatem A. Raswan
• Abstract summary: Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Most of the existing systems are semi-automated and need to manually select the lung and nodules regions. We proposed a fully automated end-to-end lung nodule detection and segmentation system based on a deep learning approach.
• Score: 10.424363966870773
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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