Ervik Øyvind, Tveten Ingrid, Hofstad Erlend Fagertun, Langø Thomas, Leira Håkon Olav, Amundsen Tore, Sorger Hanne
Clinic of Medicine, Nord-Trøndelag Hospital Trust, Levanger Hospital, 7601 Levanger, Norway.
Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7030 Trondheim, Norway.
J Imaging. 2024 Aug 6;10(8):190. doi: 10.3390/jimaging10080190.
Endobronchial ultrasound (EBUS) is used in the minimally invasive sampling of thoracic lymph nodes. In lung cancer staging, the accurate assessment of mediastinal structures is essential but challenged by variations in anatomy, image quality, and operator-dependent image interpretation. This study aimed to automatically detect and segment mediastinal lymph nodes and blood vessels employing a novel U-Net architecture-based approach in EBUS images. A total of 1161 EBUS images from 40 patients were annotated. For training and validation, 882 images from 30 patients and 145 images from 5 patients were utilized. A separate set of 134 images was reserved for testing. For lymph node and blood vessel segmentation, the mean ± standard deviation (SD) values of the Dice similarity coefficient were 0.71 ± 0.35 and 0.76 ± 0.38, those of the precision were 0.69 ± 0.36 and 0.82 ± 0.22, those of the sensitivity were 0.71 ± 0.38 and 0.80 ± 0.25, those of the specificity were 0.98 ± 0.02 and 0.99 ± 0.01, and those of the F1 score were 0.85 ± 0.16 and 0.81 ± 0.21, respectively. The average processing and segmentation run-time per image was 55 ± 1 ms (mean ± SD). The new U-Net architecture-based approach (EBUS-AI) could automatically detect and segment mediastinal lymph nodes and blood vessels in EBUS images. The method performed well and was feasible and fast, enabling real-time automatic labeling.
支气管内超声（EBUS）用于胸部淋巴结的微创采样。在肺癌分期中，纵隔结构的准确评估至关重要，但受到解剖结构变异、图像质量和依赖操作者的图像解读的挑战。本研究旨在采用一种基于新型U-Net架构的方法，在EBUS图像中自动检测和分割纵隔淋巴结及血管。共对40例患者的1161张EBUS图像进行了标注。用于训练和验证的图像中，30例患者的882张图像以及5例患者的145张图像被使用。另外保留了134张图像用于测试。对于淋巴结和血管分割，Dice相似系数的均值±标准差（SD）分别为0.71±0.35和0.76±0.38，精度分别为0.69±0.36和0.82±0.22，灵敏度分别为0.71±0.38和0.80±0.25，特异性分别为0.98±0.02和0.99±0.01，F1分数分别为0.85±0.16和0.81±0.21。每张图像的平均处理和分割运行时间为55±1毫秒（均值±标准差）。基于新型U-Net架构的方法（EBUS-AI）能够在EBUS图像中自动检测和分割纵隔淋巴结及血管。该方法性能良好，可行且快速，能够实现实时自动标注。
