نویسنده
Ugurdag, H. Fatih, Ates, Hasan F., Guven, Gokce
تاریخ انتشار
2024-01-01
محل انتشار
-
IEEE
موضوع
Vision transformers, Neural implicit methods, 3D organ topology, X-ray, 3D reconstruction
نوع
دوره ای
زبان
انگلیسی
دیجیتال
بله
نسخه خطی
خیر
کتابخانه
دانشگاه اوزیغین
شناسه دارایی کتابخانه
2169-3536
شماره ثبت
bd5e6915-9f98-424a-8831-13adea686484
محل کتابخانه
Computer Science, Electrical & Electronics Engineering
تاریخ
2024-01-01
متن نمونه
In this work, an innovative approach is proposed for three-dimensional (3D) organ volume reconstruction from a single planar X-ray, namely X2V network. Such capability holds pivotal clinical potential, especially in real-time image-guided radiotherapy, computer-aided surgery, and patient follow-up sessions. Traditional methods for 3D volume reconstruction from X-rays often require the utilization of statistical 3D organ templates, which are employed in 2D/3D registration. However, these methods may not accurately account for the variation in organ shapes across different subjects. Our X2V model overcomes this problem by leveraging neural implicit representation. A vision transformer model is integrated as an encoder network, specifically designed to direct and enhance attention to particular regions within the X-ray image. The reconstructed meshes exhibit a similar topology to the ground truth organ volume, demonstrating the ability of X2V in accurately capturing the 3D structure from a 2D image. The effectiveness of X2V is evaluated on lung X-rays using several metrics, including volumetric Intersection over Union (IoU). X2V outperforms the state-of-the-art method in the literature for lungs (DeepOrganNet) by about 7-9% achieving IoU's between 0.892-0.942 versus DeepOrganNet's IoU of 0.815-0.888.
DOI
10.1109/ACCESS.2024.3385668
Cilt
12
