Department of Ultrasound, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
School of Data Science and Engineering, East China Normal University, Shanghai, China.
J Appl Clin Med Phys. 2024 Jun;25(6):e14332. doi: 10.1002/acm2.14332. Epub 2024 Mar 25.
A well display of the spatial location of thyroid nodules in the thyroid is important for surgical path planning and surgeon-patient communication. The aim of this study was to establish a three-dimensional (3D) reconstruction method of the thyroid gland, thyroid nodule, and carotid artery with automatic detection based on two-dimensional (2D) ultrasound videos, and to evaluate its clinical value.
Ultrasound videos, including the thyroid gland with nodule, isthmus of thyroid gland, and ipsilateral carotid artery, were recorded. BC-UNet, MTN-Net, and RDPA-U-Net network models were innovatively employed for segmentation of the thyroid glands, the thyroid nodules, and the carotid artery respectively. Marching Cubes algorithm was used for reconstruction, while Laplacian smoothing algorithm was employed to smooth the 3D model surface. Using this model, 20 patients and 15 surgeons completed surveys on the effectiveness of this model for the pre-surgery demonstration of nodule location as well as surgeon-patient communication.
The thyroid gland with nodule, isthmus of gland, and carotid artery were reconstructed and displayed. With the 3D model, the understanding of the spatial location of thyroid nodules improved in all three surgeon groups, eliminating the influence of professional levels. In the patient survey, the patients' understanding of the thyroid nodule location and procedure for surgery were significantly improved. In addition, with the 3D model, the time for doctors to explain to patients was significantly reduced (16.75 vs. 8.85 min, p = 0.001).
To our knowledge, this is the first report of constructing a 3D thyroid model using a deep learning technique for personalized thyroid segmentation based on 2D ultrasound videos. The preliminary clinical application showed that it was conducive to the comprehension of the location of thyroid nodules for surgeons and patients, with significant improvement on the efficiency of surgeon-patient communication.
甲状腺结节在甲状腺中的空间位置的良好显示对于手术路径规划和医患沟通非常重要。本研究旨在建立一种基于二维（2D）超声视频的甲状腺、甲状腺结节和颈动脉的自动检测三维（3D）重建方法，并评估其临床价值。
记录包括甲状腺结节、甲状腺峡部和同侧颈动脉在内的超声视频。分别采用 BC-UNet、MTN-Net 和 RDPA-U-Net 网络模型对甲状腺、甲状腺结节和颈动脉进行分割。使用 Marching Cubes 算法进行重建，然后使用拉普拉斯平滑算法对 3D 模型表面进行平滑处理。使用该模型，20 名患者和 15 名外科医生完成了对该模型在结节位置术前演示和医患沟通方面有效性的调查。
重建并显示了带结节的甲状腺、腺峡部和颈动脉。通过 3D 模型，所有三组外科医生都能更好地理解甲状腺结节的空间位置，消除了专业水平的影响。在患者调查中，患者对甲状腺结节位置和手术过程的理解明显提高。此外，通过 3D 模型，医生向患者解释的时间明显减少（16.75 分钟 vs. 8.85 分钟，p = 0.001）。
据我们所知，这是首次报道使用深度学习技术基于 2D 超声视频构建个性化甲状腺分割的 3D 甲状腺模型。初步临床应用表明，它有利于外科医生和患者理解甲状腺结节的位置，显著提高了医患沟通的效率。
