PDF(Pubmed)
Abstract：
Elbow computerized tomography (CT) scans have been widely applied for describing elbow morphology. To enhance the objectivity and efficiency of clinical diagnosis, an automatic method to recognize, segment, and reconstruct elbow joint bones is proposed in this study. The method involves three steps: initially, the humerus, ulna, and radius are automatically recognized based on the anatomical features of the elbow joint, and the prompt boxes are generated. Subsequently, elbow MedSAM is obtained through transfer learning, which accurately segments the CT images by integrating the prompt boxes. After that, hole-filling and object reclassification steps are executed to refine the mask. Finally, three-dimensional (3D) reconstruction is conducted seamlessly using the marching cube algorithm. To validate the reliability and accuracy of the method, the images were compared to the masks labeled by senior surgeons. Quantitative evaluation of segmentation results revealed median intersection over union (IoU) values of 0.963, 0.959, and 0.950 for the humerus, ulna, and radius, respectively. Additionally, the reconstructed surface errors were measured at 1.127, 1.523, and 2.062 mm, respectively. Consequently, the automatic elbow reconstruction method demonstrates promising capabilities in clinical diagnosis, preoperative planning, and intraoperative navigation for elbow joint diseases.
摘要：
肘部计算机断层扫描（CT）扫描已广泛用于描述肘部形态。提高临床诊断的客观性和效率,一种自动识别方法，段,并重建肘关节骨是在这项研究中提出的。该方法包括三个步骤：最初，肱骨,尺骨,根据肘关节的解剖特征自动识别半径，并生成提示框。随后，肘部MedSAM是通过迁移学习获得的，通过整合提示框来准确分割CT图像。之后,执行孔填充和对象重新分类步骤以细化掩模。最后,三维(3D)重建是无缝地使用行进立方体算法进行。为了验证该方法的可靠性和准确性,这些图像与高级外科医生标记的面具进行了比较。对分割结果的定量评估显示，肱骨的平均交集联合（IoU）值为0.963、0.959和0.950，尺骨,和半径，分别。此外,重建的表面误差分别为1.127、1.523和2.062mm，分别。因此,自动肘部重建方法在临床诊断中显示出有希望的能力，术前计划,肘关节疾病的术中导航。
