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http://dx.doi.org/10.14372/IEMEK.2022.17.5.265

Boundary and Reverse Attention Module for Lung Nodule Segmentation in CT Images  

Hwang, Gyeongyeon (Jeonbuk National University)
Ji, Yewon (Jeonbuk National University)
Yoon, Hakyoung (Jeonbuk National University)
Lee, Sang Jun (Jeonbuk National University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.17, no.5, 2022 , pp. 265-272 More about this Journal
Abstract
As the risk of lung cancer has increased, early-stage detection and treatment of cancers have received a lot of attention. Among various medical imaging approaches, computer tomography (CT) has been widely utilized to examine the size and growth rate of lung nodules. However, the process of manual examination is a time-consuming task, and it causes physical and mental fatigue for medical professionals. Recently, many computer-aided diagnostic methods have been proposed to reduce the workload of medical professionals. In recent studies, encoder-decoder architectures have shown reliable performances in medical image segmentation, and it is adopted to predict lesion candidates. However, localizing nodules in lung CT images is a challenging problem due to the extremely small sizes and unstructured shapes of nodules. To solve these problems, we utilize atrous spatial pyramid pooling (ASPP) to minimize the loss of information for a general U-Net baseline model to extract rich representations from various receptive fields. Moreover, we propose mixed-up attention mechanism of reverse, boundary and convolutional block attention module (CBAM) to improve the accuracy of segmentation small scale of various shapes. The performance of the proposed model is compared with several previous attention mechanisms on the LIDC-IDRI dataset, and experimental results demonstrate that reverse, boundary, and CBAM (RB-CBAM) are effective in the segmentation of small nodules.
Keywords
CT image analysis; Lung nodule; Small object detection; Attention mechanism;
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