A review on automatic fetal and neonatal brain MRI segmentation

By Antonios Makropoulos, Serena J. Counsell, Daniel Rueckert. 2017

207 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Pediatrics, Perinatology and Child Health

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Arvid Lundervold
2. Jonathan Passerat-Palmbach
3. Ismail Ben Ayed
4. Emma Claire Robinson
5. Jose Dolz
6. Daniel Rueckert
7. Christian Desrosiers
8. Antonios Makropoulos

In Pediatrics, Perinatology and Child Health

1. Arvid Lundervold
2. Daniel Rueckert
3. Antonios Makropoulos
4. Serena J. Counsell

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, background): “ image segmentation and tissue classication [250, 251, 252, 253]. An impressive range of segmentation methods and approaches have been reported (especially for brain segmentation) and reviewed, e.g. [254, 255, 256, 257, 258, 259, 260, 261, 262]. MR image segmentation using deep learning approaches, ”
• HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation (2018, background): “Atlas-propagation approaches are commonly used in multi-modal scenarios [34], [35].”
• The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction (2017, background): “A review of neonatal segmentation methods can be found in Devi et al. (2015); Makropoulos et al. (2017).”