Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain

By Antonios Makropoulos, Ioannis S. Gousias, Christian Ledig, Paul Aljabar, A. Serag, Joseph V. Hajnal, A. David Edwards, Serena J. Counsell, Daniel Rueckert. IEEE Transactions on Medical Imaging, 2014

388 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Pediatrics, Perinatology and Child Health

View Paper: https://www.semanticscholar.org/paper/454ca5266a6a47db06be0dd8a1faed88f9ad2de3

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Mert Rory Sabuncu
2. Matthew F. Glasser
3. Juan Eugenio Iglesias
4. Daniel Rueckert
5. Mark Jenkinson
6. Ben Glocker
7. Stephen M. Smith
8. D. V. Van Essen

In Pediatrics, Perinatology and Child Health

1. Mert Rory Sabuncu
2. Juan Eugenio Iglesias
3. Daniel Rueckert
4. Antonios Makropoulos
5. Mark Jenkinson
6. Stephen M. Smith

Citations in the corpus

• CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019, methodology): “MEDICAL image segmentation is often an important step in medical image analysis, such as optic disc segmentation [1]–[3] and blood vessel detection [4]–[8] in retinal images, cell segmentation [9]–[11] in electron microscopic (EM) recordings, lung segmentation [12]–[16] and brain segmentation [17]–[”
• A review on automatic fetal and neonatal brain MRI segmentation (2017, neutral): “, 2010) are commonly used to perform INU correction in the perinatal brain (Makropoulos et al., 2014; Wu et al., 2014; Tourbier et al., 2015; Wang et al., 2015; Rajchl et al., 2016; Serag et al., 2016).”
• Multimodal Surface Matching with Higher-Order Smoothness Constraints☆ (2017, methodology): “The resulting 20 segmentations were then fused in a locally-weighted scheme to form the subject’s cortical labels (see LWV-MSD in Artaechevarria et al. (2009); Makropoulos et al. (2014)), so that similar patches between each atlas and the image have increased weighting.”
• Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “MAS has also been employed for the segmentation of cortical and subcortical structures in MRI data from fetuses, neonates, and infants too (Gholipour et al., 2012; Gousias et al., 2008, 2010, 2013; Shi et al., 2010; Wang et al., 2014e; Li et al., 2014; Koch et al., 2014; Makropoulos et al., 2014; Wa”
• Automatic Segmentation of MR Brain Images With a Convolutional Neural Network (2016, neutral): “A popular approach for brain image segmentation is the use of (population specific) atlases [10]–[17], but also pattern recognition methods are used [18], [19], sometimes in”