Automated object recognition in medical images can facilitate medical diagnosis and treatment. In this paper, we automatically segmented supraclavicular nerves in ultrasound images to assist in injecting peripheral nerve blocks. Nerve blocks are generally used for pain treatment after surgery, where ultrasound guidance is used to inject local anesthetics next to target nerves. This treatment blocks the transmission of pain signals to the brain, which can help improve the rate of recovery from surgery and significantly decrease the requirement for postoperative opioids. However, Ultrasound Guided Regional Anesthesia (UGRA) requires anesthesiologists to visually recognize the actual nerve position in the ultrasound images. This is a complex task given the myriad visual presentations of nerves in ultrasound images, and their visual similarity to many neighboring tissues. In this study, we used an automated nerve detection system for the UGRA Nerve Block treatment. The system can recognize the position of the nerve in ultrasound images using Deep Learning techniques. We developed a model to capture features of nerves by training two deep neural networks with skip connections: two extended U-Net architectures with and without dilated convolutions. This solution could potentially lead to an improved blockade of targeted nerves in regional anesthesia.
翻译:医疗图象中的自动物体识别可以促进医疗诊断和治疗。 在本文中,我们在超声波图像中自动分解超光谱神经,以协助注射外围神经块。 神经区块通常用于手术后的疼痛治疗,在手术后用于超声导指导,用来给目标神经旁边的当地麻醉剂注射超声导。 这种治疗阻止了将疼痛信号传送到大脑,这有助于提高手术康复率,并大大减少对手术后类阿片的需求。 然而,超声导区域麻醉(UGRA)需要麻醉师对超声波图像中的实际神经位置进行视觉识别。 鉴于超声波图像中神经的视觉表现繁多,以及它们与许多相邻组织的视觉相似性,这是一个复杂的任务。 在这项研究中,我们使用自动神经检测系统对UGRA Nerve Block治疗进行检测,这可以帮助提高手术后的恢复速度,并大大降低对手术后类阿片的需求。 然而,我们开发了一种模型,通过训练两个有超音网连接的深层神经特征特征:两个U-Net扩展的神经结构结构,可以升级到潜在的神经系统。