Kevin Keraudren

TL;DR: A novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target and ensures high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations.

TL;DR: This work proposes an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion, and combines this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds.

TL;DR: A standardized evaluation framework is introduced to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE and showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices.

TL;DR: This work proposes a method for accurate and robust localisation of the fetal brain in MRI when the image data is acquired as a stack of 2D slices misaligned due to fetal motion, which allows a robust detection even in the presence of substantial fetal motion.

TL;DR: This paper proposes a method to facilitate fully automatic brain voxel classification by means of rotation invariant volume descriptors and shows how the classification process can be used for a direct segmentation of the brain by simple refinement methods within the raw MR scan data.