Kongbin Kang

Yehyun Suh, Lin Li, Aric Plumley et al.

Accurate matching of pedicle screws in both anteroposterior (AP) and lateral (LAT) images is critical for successful spinal decompression and stabilization during surgery. However, establishing screw correspondence, especially in LAT views, remains a significant clinical challenge. This paper introduces a method to address pedicle screw correspondence and pose estimation from dual C-arm images. By comparing screw combinations, the approach demonstrates consistent accuracy in both pairing and registration tasks. The method also employs 2D-3D alignment with screw CAD 3D models to accurately pair and estimate screw pose from dual views. Our results show that the correct screw combination consistently outperforms incorrect pairings across all test cases, even prior to registration. After registration, the correct combination further enhances alignment between projections and images, significantly reducing projection error. This approach shows promise for improving surgical outcomes in spinal procedures by providing reliable feedback on screw positioning.

Yunhe Xie, Kongbin Kang, Gregory Sharp et al.

Image guidance has been widely used in radiation therapy. Correctly identifying the bounding box of the anatomical landmarks from limited field of views is the key to success. In image-guided radiation therapy (IGRT), the detection of those landmarks like the 12th vertebra (T12) still requires tedious manual inspections and annotations; and superior-inferior misalignment to the wrong vertebral body is still relatively common. It is necessary to develop an automated approach to detect those landmarks from images. The challenges of training a model to identify T12 vertebrae automatically mainly are high shape similarity between T12 and neighboring vertebrae, limited annotated data, and class imbalance. This study proposed a novel 3D detection network, requiring only a small amount of training data. Our approach has the following innovations, including 1) the introduction of an auxiliary network to build constraint feature map for improving the model's generalization, especially when the constraint structure is easier to be detected than the main one; 2) an improved detection head and target functions for accurate bounding box detection; and 3) an improved loss functions to address the high class imbalance. Our proposed network was trained, validated and tested on anotated CT images from 55 patients and demonstrated accurate distinguish T12 vertebra from its neighboring vertebrae of high shape similarity. Our proposed algorithm yielded the bounding box center and size errors of 3.98\pm2.04mm and 16.83\pm8.34mm, respectively. Our approach significantly outperformed state-of-the-arts Retina-Net3D in average precision (AP) at IoU thresholds of 0.35 and 0.5, with AP increasing from 0 to 95.4 and 0 to 64.7, respectively. In summary, our approach has a great potential to be integrated into the clinical workflow to improve the safety of IGRT.