Knowledge of what dose-volume histograms can be expected for a previously unseen patient could increase consistency and quality in radiotherapy treatment planning. We propose a machine learning method that uses previous treatment plans to predict such dose-volume histograms. The key to the approach is the framing of dose-volume histograms in a probabilistic setting.The training consists of esti...

Nowadays, motion estimation is one of the main subjects in computer vision. Many methods developed to compute motion make use of the optical flow hypothesis. These methods usually fail to capture motion of objects with intensity evolution. We propose a new approach to solve the motion computation problem with a different type of constancy hypothesis. Because we are mainly interested in deformab...

We propose a fast algorithm to estimate brain tissue concentrations from conventional T1-weighted images based on a Bayesian maximum a posteriori formulation that extends the "mixel" model developed in the 90's. A key observation is the necessity to incorporate additional prior constraints to the "mixel" model for the estimation of plausible concentration maps. Experiments on the ADNI standardi...

BACKGROUND/AIMS To determine which biometric parameters provide optimum predictive power for ocular volume. METHODS Sixty-seven adult subjects were scanned with a Siemens 3-T MRI scanner. Mean spherical error (MSE) (D) was measured with a Shin-Nippon autorefractor and a Zeiss IOLMaster used to measure (mm) axial length (AL), anterior chamber depth (ACD) and corneal radius (CR). Total ocular v...

Motivated by problems of uncertainty propagation and robust estimation we are interested in computing a polynomial sublevel set of fixed degree and minimum volume that contains a given semialgebraic set K. At this level of generality this problem is not tractable, even though it becomes convex e.g. when restricted to nonnegative homogeneous polynomials. Our contribution is to describe and justi...

This paper presents a new method for partial volume estimation using standard eigenimage method and B-splines. The proposed method is applied on the multi-parameter volumetric images such as MRI. The proposed approach uses the B-spline bases (kernels) to interpolate a continuous 2D surface or 3D density function for a sampled image dataset. It uses the Fourier domain to calculate the interpolat...

Gate volume is an integral part of many algorithms for non-parametric target tracking in clutter. Non-parametric target tracking assumes no a-priori knowledge of clutter measurement density. In the simplest case of a single-target single-state target tracking filter employing a Gaussian approximation, the gate is a hyper-ellipsoid whose volume can be calculated analytically. However, in a multi...

Introduction Left atrial volume (LaV) is an important predictor of outcome for many cardiovascular conditions. The most commonly used method to estimate LaV is the area-length (AL) biplane formula based upon LA measures from 2 and 4 chamber (CH) views. Given the asymetrical shape of the LA, we hypothesized that a formula incorporating measures from all 3 conventional long-axis views is a more a...