[논문]CT 영상에서 폐 결절 분할을 위한 경계 및 역 어텐션 기법

황경연; 지예원; 윤학영; 이상준

doi:10.14372/iemek.2022.17.5.265

CT 영상에서 폐 결절 분할을 위한 경계 및 역 어텐션 기법
Boundary and Reverse Attention Module for Lung Nodule Segmentation in CT Images 원문보기

대한임베디드공학회논문지 = IEMEK Journal of embedded systems and applications, v.17 no.5, 2022년, pp.265 - 272

황경연 (Jeonbuk National University) , 지예원 (Jeonbuk National University) , 윤학영 (Jeonbuk National University) , 이상준 (Jeonbuk National University)

Abstract ▼ AI-Helper

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.

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표/그림 (5)

그림 그림 1. 폐 결절의 CT영상 및 label 영상 Fig. 1. CT and label images of lung nodules
그림 그림 2. ASPP과 RB-CBAM을 이용한 pretrained U-Net 구조 Fig. 2. Pretrained U-Net architecture using ASPP and RB-CBAM
그림 그림 3. SE module, Attention U-Net module, BAM, CBAM, RB-CBAM의 분할 결과 Fig. 3. Results of segmentation of SE module, Attention U-Net module, BAM, CBAM, and RB-CBAM
표 표 1. Pretrained U-Net기반 attention module 평가 표 Table 1. Pretrained U-Net based attention module evaluation table
표 표 2. ASPP, reverse attention, boundary attention의 유무에 따른 분할 성능 비교 Table 2. Comparison of segmentation performance with and without ASPP, reverse attention, and boundary attention

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DOI : 10.14372/IEMEK.2022.17.5.265 [무료]
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