Generation of Artificial Vessel Trees for X-ray Image Analysis

Description

For the training of modern deep-learning-based image analysis algorithms, large scale datasets are required. For example, the segment anything model was trained 11 million images [1]. In the field of interventional X-ray imaging, this scale of data is unattainable, since not that many interventional procedures are performed, the images are not routinely stored, and are acquired as little as possible due to dose concerns. Furthermore, for some image analysis problems manual annotation of ground truth is infeasible even for optical computer vision. In such cases, synthetic data is used frequently, like the flying chairs dataset for optical flow [2].

For vessels synthesis, simple procedural approaches exist like the Sapling Tree Gen sampling algorithm [3] and space-filling [8]. For liver vessel analysis applications, an algorithm for generation of hepatic arterial trees is available [4]. In [5], hepatic vascular structure is generated using a physiological model of vessel growth. Generative learning-based methods have also been developed [6,9].

In this work, a pipeline for training and evaluating 2d X-ray image analysis methods is developed. The starting point is a procedural 3d vessel tree synthesis method [4,8]. As an exemplary organ, liver is used. The next step in the pipeline is forward projection to a 2d image according to the geometry of a real C-arm. As potential labels for the 2d images, depth, vessel segment IDs, vessel centerline IDs and overlap information are created. Finally, to demonstrate the applicability of the vessel tree for image analysis tasks, a vessel registration approach is evaluated on the resulting 2d vessel maps [2,7,10].

Work Packages

Literature research on simulation of vessel tree structures
Implementation in prototype environment (Python, Matlab, C++, …)
Quantitative and qualitative evaluation of registration using synthetic ground truth
Discussions of results & comparison to state of the art

Public software libraries should be used where possible. The complete source code including documentation must be provided.

Literature

[1] A. Kirillov et al., “Segment Anything,” in IEEE International Conference on Computer Vision (ICCV), 2023

[2] A. Dosovitskiy et al., “FlowNet: Learning Optical Flow with Convolutional Networks,” in IEEE International Conference on Computer Vision (ICCV), 2015

[3] N. Maul et al., “Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model,” in International Conference on Information Processing in Medical Imaging, 2023

[4] J. F. Whitehead, P. F. Laeseke, S. Periyasamy, M. A. Speidel, and M. G. Wagner, “In silico simulation of hepatic arteries: An open-source algorithm for efficient synthetic data generation,” Medical Physics, 2023

[5] M. Kretowski, Y. Rolland, J. Bézy-Wendling, and J.-L. Coatrieux, “Physiologically based modeling of 3-D vascular networks and CT scan angiography,” IEEE Transactions on Medical Imaging, 2003

[6] T. P. Kuipers, P. R. Konduri, H. Marquering, and E. J. Bekkers, “Generating Cerebral Vessel Trees of Acute Ischemic Stroke Patients using Conditional Set-Diffusion,” in Medical Imaging with Deep Learning, 2024

[7] G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, und A. V. Dalca, „Voxelmorph: a learning framework for deformable medical image registration“, IEEE Transactions on Medical Imaging, 2019

[8] N. Rauch and M. Harders, „Interactive Synthesis of 3D Geometries of Blood Vessels.“, in Eurographics (Short Papers), 2021

[9] C. Prabhakar et al., „3D Vessel Graph Generation Using Denoising Diffusion“, in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024

[10] S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. Pluim, “Elastix: a toolbox for intensity-based medical image registration,” IEEE Transactions on Medical Imaging, 2009