An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset

1 code implementation • 29 Oct 2020 • Kelly Payette, Priscille de Dumast, Hamza Kebiri, Ivan Ezhov, Johannes C. Paetzold, Suprosanna Shit, Asim Iqbal, Romesa Khan, Raimund Kottke, Patrice Grehten, Hui Ji, Levente Lanczi, Marianna Nagy, Monika Beresova, Thi Dao Nguyen, Giancarlo Natalucci, Theofanis Karayannis, Bjoern Menze, Meritxell Bach Cuadra, Andras Jakab

It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.

Brain Segmentation Segmentation

Developing Brain Atlas through Deep Learning

1 code implementation • 10 Jul 2018 • Asim Iqbal, Romesa Khan, Theofanis Karayannis

Neuroscientists have devoted significant effort into the creation of standard brain reference atlases for high-throughput registration of anatomical regions of interest.

Exploring Brain-wide Development of Inhibition through Deep Learning

1 code implementation • 9 Jul 2018 • Asim Iqbal, Asfandyar Sheikh, Theofanis Karayannis

We introduce here a fully automated convolutional neural network-based method for brain image processing to Detect Neurons in different brain Regions during Development (DeNeRD).