Three-dimensional skeletonization and symbolic description in vascular imaging: preliminary results.

OBJECTIVE: A general method was developed to analyze and describe tree-like structures needed for evaluation of complex morphology, such as the cerebral vascular tree. Clinical application of the method in neurosurgery includes planning of the surgeon's intraoperative gestures. METHOD: We have developed a 3D skeletonization method adapted to tubular forms with symbolic description. This approach implements an iterative Dijkstra minimum cost spanning tree, allowing a branch-by-branch skeleton extraction. The proposed method was implemented using the laboratory software platform (ArtiMed). The 3D skeleton approach was tested on simulated data and preliminary trials on clinical datasets mainly based on magnetic resonance image acquisitions. RESULTS: A specific experimental evaluation plan was designed to test the skeletonization and symbolic description methods. Accuracy was tested by calculating the positioning error, and robustness was verified by comparing the results on a series of 18 rotations of the initial volume. Accuracy evaluation showed a Haussdorff's distance always smaller than 17 voxels and Dice's similarity coefficient greater than 70 %. CONCLUSION: Our method of symbolic description enables the analysis and interpretation of a vascular network obtained from angiographic images. The method provides a simplified representation of the network in the form of a skeleton, as well as a description of the corresponding information in a tree-like view.

Recent advances in cerebrovascular simulation and neuronavigation for the optimization of intracranial aneurysm clipping.

Endovascular treatment of intracranial aneurysms (IAs) has improved to the extent that in some instances such an approach has now become safer than surgery. This has dramatically changed clinical practice by reducing the volume and increasing the complexity of IAs referred for open surgical treatment. We review the simulation techniques and dedicated vascular neuronavigation systems that have been developed to maintain the quality of aneurysm clipping in this context. Simulation of surgical approaches was made possible by the introduction of high-resolution 3D imaging techniques such as three-dimensional CT angiography (3D-CTA) and three-dimensional digital subtraction angiography (3D-DSA), enabling reproduction of the craniotomy and rotation of the vascular tree according to the orientation of the operative microscope. A virtual simulator for compiling such data, the Dextroscope®, is now available for this purpose. Simulation of final clipping has been investigated through virtual or physical models, enabling anticipation of aneurysm deformation during clip application and selection of the appropriate clip(s) in terms of number, size, shape and orientation. To improve surgical dissection guidance, specific cerebrovascular neuronavigation procedures have been developed based on 3D-CTA or 3D-DSA. These help make the operation secure by accurately predicting the location and orientation of an aneurysm within its parenchymal and vascular environment. Future simulators dedicated to cerebrovascular procedures will need to integrate representation of the brain surface and biomechanical modeling of brain and aneurysm wall deformation under retraction or during clipping. They should contribute to training and maintenance of surgical skills, thereby optimizing the quality of surgical treatment in this field.

Dijkstra's algorithm applied to 3D skeletonization of the brain vascular tree: evaluation and application to symbolic.

This paper describes the methodology and the evaluation of a 3D skeletonization algorithm applied on brain vascular structure. This method is based on the application of the minimum cost-spanning tree using Dijkstra's algorithm and seems well appropriate to tubular objects. We briefly describe the different steps, from the segmentation to the skeleton analysis. Besides, we propose an original evaluation scheme of the method based on digital phantom and clinical data. The final aim of this work is to provide a symbolic description framework applied to cerebro-vascular structures.