Abdelhakim Ouaalam

TL;DR: An algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain is developed by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age and is available online as a reference for anatomy and for registration and segmentation.

TL;DR: In this paper, a 2D U-Net and autocontext based segmentation method was proposed to segment the fetal brain in real-time while the fetal MRI slices are being acquired.

TL;DR: A deep fully convolutional neural network based on 2D U-net and autocontext that can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired and can enable real- time motion tracking, motion detection, and 3D reconstruction of fetal brain MRI.

TL;DR: In this article, a new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections was proposed for cortical plate segmentation.

TL;DR: A new and powerful deep learning segmentation method that exploits new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections to facilitate and accelerate large-scale studies on normal and altered fetal brain cortical maturation and folding.