713 citations · 1.4k across the 13 of their papers we have counts for
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FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI
Santiago Estrada, David Kügler, Emad Bahrami +5
The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioural, and cognitive functions. However, despite its importance, only a few small-s…
An automated, geometry-based method for hippocampal shape and thickness analysis
Kersten Diers, Hannah Baumeister, Frank Jessen +3
The hippocampus is one of the most studied neuroanatomical structures due to its involvement in attention, learning, and memory as well as its atrophy in ageing, neurological, and…
Rapid head-pose detection for automated slice prescription of fetal-brain MRI
Malte Hoffmann, Esra Abaci Turk, Borjan Gagoski +8
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical ass…
Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI
Santiago Estrada, Ran Lu, Kersten Diers +5
The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function. The lack of an automatic proce…
FatSegNet : A Fully Automated Deep Learning Pipeline for Adipose Tissue Segmentation on Abdominal Dixon MRI
Santiago Estrada, Ran Lu, Sailesh Conjeti +4
Purpose: Development of a fast and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify abdominal adipose tissue on Dixon MRI from the R…
Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks
Santiago Estrada, Sailesh Conjeti, Muneer Ahmad +2
Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance…
Complex Fully Convolutional Neural Networks for MR Image Reconstruction
Muneer Ahmad Dedmari, Sailesh Conjeti, Santiago Estrada +3
Undersampling the k-space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI). Current deep learning based approaches for supervised learning of MRI image r…
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
Christian Wachinger, Martin Reuter, Tassilo Klein
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learn…