Maria A. Zuluaga

Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian–Eulerian PDE approach using partial volume maps

Accurate cortical thickness estimation is important for the study of many neurodegenerative diseases. Many approaches have been previously proposed, which can be broadly categorised as mesh-based and voxel-based. While the mesh-based approaches can potentially achieve subvoxel resolution, they usually lack the computational efficiency needed for clinical applications and large database studies. In contrast, voxel-based approaches, are computationally efficient, but lack accuracy. The aim of this paper is to propose a novel voxel-based method based upon the Laplacian definition of thickness that is both accurate and computationally efficient. A framework was developed to estimate and integrate the partial volume information within the thickness estimation process. Firstly, in a Lagrangian step, the boundaries are initialized using the partial volume information. Subsequently, in an Eulerian step, a pair of partial differential equations are solved on the remaining voxels to finally compute the thickness. Using partial volume information significantly improved the accuracy of the thickness estimation on synthetic phantoms, and improved reproducibility on real data. Significant differences in the hippocampus and temporal lobe between healthy controls (NC), mild cognitive impaired (MCI) and Alzheimers disease (AD) patients were found on clinical data from the ADNI database. We compared our method in terms of precision, computational speed and statistical power against the Eulerian approach. With a slight increase in computation time, accuracy and precision were greatly improved. Power analysis demonstrated the ability of our method to yield statistically significant results when comparing AD and NC. Overall, with our method the number of samples is reduced by 25% to find significant differences between the two groups.

Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading.

This paper describes an evaluation framework that allows a standardized and objective quantitative comparison of carotid artery lumen segmentation and stenosis grading algorithms. We describe the data repository comprising 56 multi-center, multi-vendor CTA datasets, their acquisition, the creation of the reference standard and the evaluation measures. This framework has been introduced at the MICCAI 2009 workshop 3D Segmentation in the Clinic: A Grand Challenge III, and we compare the results of eight teams that participated. These results show that automated segmentation of the vessel lumen is possible with a precision that is comparable to manual annotation. The framework is open for new submissions through the website http://cls2009.bigr.nl.

Multi-atlas propagation whole heart segmentation from MRI and CTA using a local normalised correlation coefficient criterion

Accurate segmentation of the whole heart from 3D image sequences is an important step in the developement of clinical applications. As manual delineation is a tedious task that is prone to errors and dependant on the expertise of the observer, fully automated segmentation methods are highly desirable. In this work, we present a fully automated method for the segmentation of the whole heart and the great vessels from 3D images. The method is based on a muti-atlas propagation segmentation scheme, that has been proven to be succesful in brain segmentation. Based on a cross correlation metric, our method selects the best atlases for propagation allowing the refinement of the segmentation at each iteration of the propagation. We show that our method allows segmentation from multiple image modalities by validating it on computed tomography angiography (CTA) and magnetic resonance images (MRI). Our results are comparable to state-of-the-art methods on CTA and MRI with average Dice scores of 90.9% and 89.0% for the whole heart when evaluated on a 23 and 8 cases, respectively.

Fuzzy classification of brain MRI using a priori knowledge: weighted fuzzy C-means

We report in this communication a new formulation for the cost function of the well-known fuzzy C-means classification technique whereby we introduce weights. We derive the equations of this new weighted fuzzy C-means algorithm (WFCM) in the presence of additive and multiplicative bias field. We show that the weights can be designed in the same manner as prior probabilities commonly used in maximum a posteriori classifier (MAP) to introduce prior knowledge (e.g. using atlas), and increase robustness to noise (e.g. using Markov random field). Using prior probabilities of three popular MAP algorithms, we compare the performances of our proposed WFCM scheme using the simulated MRI T1W BrainWeb datasets, as well as five T1W MR patient scans. Our results show that WFCM achieves superior performances for low SNR conditions, whereas a Gaussian mixture model is desirable for high noise levels. WFCM allows rigorous comparison of fuzzy and probabilistic classifiers, and offers a framework where improvements can be shared between those two types of classifier.

A Computer Assisted Planning System for the Placement of sEEG Electrodes in the Treatment of Epilepsy

Approximately 20–30% of patients with focal epilepsy are medically refractory and may be candidates for curative surgery. Stereo EEG is the placement of multiple depth electrodes into the brain to record seizure activity and precisely identify the area to be resected. The two important criteria for electrode implantation are accurate navigation to the target area, and avoidance of critical structures such as blood vessels. In current practice neurosurgeons have no assistance in the planning of the electrode trajectories.

Stability, structure and scale: improvements in multi-modal vessel extraction for SEEG trajectory planning

PurposeBrain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying significantly associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer-assisted planning systems that can optimise the safety profile of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system.MethodsThe developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels.ResultsTwelve paired data sets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coefficient was

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

0.89\pm 0.04

Slic-Seg: Slice-by-Slice Segmentation Propagation of the Placenta in Fetal MRI Using One-Plane Scribbles and Online Learning

0.89±0.04, representing a statistically significantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (