X2V: 3D organ volume reconstruction from a planar X-Ray image with neural implicit methods

Name: X2V: 3D organ volume reconstruction from a planar X-Ray image with neural implicit methods
Author: Ugurdag, H. Fatih, Ates, Hasan F., Guven, Gokce

İsim	X2V: 3D organ volume reconstruction from a planar X-Ray image with neural implicit methods
Yazar	Ugurdag, H. Fatih, Ates, Hasan F., Guven, Gokce
Basım Tarihi:	2024-01-01
Basım Yeri	- IEEE
Konu	Vision transformers, Neural implicit methods, 3D organ topology, X-ray, 3D reconstruction
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2169-3536
Kayıt Numarası	bd5e6915-9f98-424a-8831-13adea686484
Lokasyon	Computer Science, Electrical & Electronics Engineering
Tarih	2024-01-01
Örnek Metin	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

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

X2V: 3D organ volume reconstruction from a planar X-Ray image with neural implicit methods

Yazar Ugurdag, H. Fatih, Ates, Hasan F., Guven, Gokce

Basım Tarihi 2024-01-01

Basım Yeri - IEEE

Konu Vision transformers, Neural implicit methods, 3D organ topology, X-ray, 3D reconstruction

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2169-3536

Kayıt Numarası bd5e6915-9f98-424a-8831-13adea686484

Lokasyon Computer Science, Electrical & Electronics Engineering

Tarih 2024-01-01

Örnek Metin 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