Adipose Tissue Segmentation after Lung Slice Localization in Chest CT Images Based on ConvBiGRU and Multi-Module UNet

• Published in: Biomedicines Vol. 12; no. 5; p. 1061
• Main Authors: Lei, Pengyu, Li, Jie, Yi, Jizheng, Chen, Wenjie
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.05.2024
• Subjects: Abdomen; Accuracy; Adipose tissue; Adipose tissues; Algorithms; Body fat; Body mass index; Chronic obstructive pulmonary disease; Comparative analysis; CT imaging; deep learning (DL); Diagnosis; Localization; Lung cancer; Lung diseases; Lung diseases, Obstructive; Lung transplants; Mammography; Medical imaging equipment; Medical research; Medicine, Experimental; Methods; Segmentation; subcutaneous adipose tissue (SAT); visceral adipose tissue (VAT); localization; subcutaneous adipose tissue (SAT); segmentation; visceral adipose tissue (VAT); deep learning (DL)
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines12051061

The distribution of adipose tissue in the lungs is intricately linked to a variety of lung diseases, including asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. Accurate detection and quantitative analysis of subcutaneous and visceral adipose tissue surrounding the lungs are essential for effectively diagnosing and managing these diseases. However, there remains a noticeable scarcity of studies focusing on adipose tissue within the lungs on a global scale. Thus, this paper introduces a ConvBiGRU model for localizing lung slices and a multi-module UNet-based model for segmenting subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), contributing to the analysis of lung adipose tissue and the auxiliary diagnosis of lung diseases. In this study, we propose a bidirectional gated recurrent unit (BiGRU) structure for precise lung slice localization and a modified multi-module UNet model for accurate SAT and VAT segmentations, incorporating an additive weight penalty term for model refinement. For segmentation, we integrate attention, competition, and multi-resolution mechanisms within the UNet architecture to optimize performance and conduct a comparative analysis of its impact on SAT and VAT. The proposed model achieves satisfactory results across multiple performance metrics, including the Dice Score (92.0% for SAT and 82.7% for VAT), F1 Score (82.2% for SAT and 78.8% for VAT), Precision (96.7% for SAT and 78.9% for VAT), and Recall (75.8% for SAT and 79.1% for VAT). Overall, the proposed localization and segmentation framework exhibits high accuracy and reliability, validating its potential application in computer-aided diagnosis (CAD) for medical tasks in this domain.