Moving-mask volume growing: a novel volume segmentation algorithm for medical images

Various physiological modelling studies require isolating the anatomy of interest from medical imaging. There exist a variety of segmentation algorithms, each particularly tailored for a specific anatomical region, imaging modality or physiological modelling study. Simulations of blood flow in brain vasculature require that the arterial and venous vasculature be isolated from angiographic modalities such as rotational planar angiography, computer tomography angiography and magnetic resonance angiography studies. Due to the tortuousity and apposition of the cerebral vessels to the highly vascular meninges and cranium, segmentation of these vessels is generally difficult. This work focuses on a novel algorithm for anatomy segmentation for specific use in the isolation of a particular region of the cerebrovascular anatomy associated with vessel pathology such as aneurysms and arteriovenous malformations. The algorithm uses geometric volume growing by means of comparing the intensities of neighbouring voxels originating from a user-determined seed point rather than fully automated segmentation by means of statistical algorithms that erroneously assume a normal distribution of intensity values in the image data. The research carried out shows that this novel algorithm can easily be incorporated into a simple processing chain for transoperative use by clinicians as the total time from a DICOM stack being imported to a STL geometry being created was on average under 5 minutes. Measurements carried out by clinicians of the anatomy in imaging data strongly correlate with the same measurements on the segmented volumes indicating high accuracy and precision in segmentation. In conclusion, although this algorithm is not suitable for all anatomies, modalities or applications, it shows promise for cerebrovascular studies in particular those where computer fluid dynamics or finite element method simulations of treatment are carried out. Further work should focus on improvements and tailoring the algorithm for other specific data and applications.</p

Patient-specific modelling of blood-flow in cerebral aneurysms

Cerebral aneurysms are a pathology of the cerebral vasculature in which an area of the blood vessel wall weakens and balloons. These pose a serious risk of rupture and can result in life-threatening cerebral haemorrhages. Treatment of cerebral aneurysms is either by surgical clipping or by coils or stents inserted by interventional radiological techniques. The choice of treatment is currently based on clinical experience and on the position and size of the aneurysm. Combining medical imaging with computational fluid dynamics (CFD) analysis makes possible patient-specific modelling of blood-flow within the aneurysm and surrounding vasculature and the potential to model different treatment options. A first stage in this research was to determine the feasibility of patient-specific modelling and the use of in-silico techniques to study flow for the first time in the same vessels with and without an aneurysm. Three-dimensional renderings of the cerebral blood vessels were reconstructed from computed tomography angiograms of the head using Matlab (The MathWorks) for image processing and ScanIP (Simpleware Inc.) for 3D rendering and meshing. Meshes were then imported into COMSOL Multiphysics for finite element analysis. Pulsatile blood-flow was simulated through the cerebrovascular vessels and the velocity, pressure and wall shear stress determined. This was done for both the vessels with the aneurysm and where the aneurysm had been ‘virtually’ removed. The results show a reduced shear stress on the vessel wall without the aneurysm which is consistent with the hypothesis that the wall is weakened and then subsequently balloons with the onset of hypertension