基於BEGAN和RTGAN之氣胸影像的擴增與氣胸區域的偵測

近年來，隨著人工智慧（Artificial Intelligence，簡稱AI）和相關技術的飛速發展，醫療影像AI浪潮席捲世界各地，研發深度學習於醫療影像的應用，藉由AI來輔助醫生診斷、提升流程效能，賦予醫師更大能力以提供患者更好的醫療照護，是積極前進的目標。氣胸(Pneumothorax)在急診部門並不罕見。在嚴重的情況下，如果不緊急處理，將存在致命的危險。建立一個急診醫師通過使用人工智慧科技加快診斷過程的氣胸檢測系統，將有極大助益。本研究基於生成對抗網絡(Generative Adversarial Network，簡稱GAN)深度學習演算法，包括生成器和鑑別器，由本實驗室團隊進行修改和開發一個自動標記醫學影像的程式系統，放射科技生成對抗網路(Radiologic Technology Generative Adversarial Network，簡稱RTGAN)，能在短時間內自動偵測氣胸，藉由不斷學習，RTGAN可以利用訓練少量影像數據來提高訓練後影像質量。同時本實驗中，也使用邊界平衡生成對抗網絡（Boundary Equilibrium Generative Adversarial Networks ，簡稱BEGAN）來增加影像數據量。使用RTGAN生成對抗網絡進行迭代訓練，大約3秒鐘內即可在每個影像中標記異常位置，準確性可達到近九成。系統立即快速篩選並自動標記氣胸位置，可以輔助急診部門醫師迅速制定治療指南，為患者提供優質高效的治療。

In recent years, with the rapid development of artificial intelligence (AI) and related technologies, the medical imaging AI wave swept the world, research and development of deep learning in medical imaging applications, using AI to assist doctors in diagnosis, improve process efficiency, and empower physicians Ability to provide patients with better medical care.Pneumothorax is not uncommon in the emergency department. In severe cases, there is a fatal danger if not handled urgently. To establish a pneumothorax detection system that can assist emergency doctor speed up the diagnosis process by using artificial intelligence techniques. This study modifies and developed by a GAN-based deep learning algorithm, which includes both generator and discriminator. Established a system RTGAN for labeled medical images, which immediately displayed to the physician for reference in the shortest time. Through continuous learning, it improves the image quality with training a small amount of data.The boundary balance generation adversarial network (BEGAN) was used to increase the amount of image data, and evaluate the effectiveness of improving the quality of the generated labeled image.Iterative training is performed using generative adversarial networks, labeling abnormal locations in each image in approximately 3 seconds. The accuracy of automatically labeling abnormal locations can reach nearly 90%. The system quickly screens and automatically labeled the pneumothorax. It can assist emergency physicians to quickly develop treatment guidelines and provide patients with high-quality and effective treatment.