A View on Atlas-Based Neonatal Brain MRI Segmentation

ICTMI 2017 ◽  
2019 ◽  
pp. 199-214
Author(s):  
Maryjo M. George ◽  
S. Kalaivani
Keyword(s):  
Brain Mri ◽  
Neonatal Brain ◽  
Mri Segmentation

Neonatal brain MRI segmentation: A review

2015 ◽  
Vol 64 ◽  
pp. 163-178 ◽  
Author(s):  
Chelli N. Devi ◽  
Anupama Chandrasekharan ◽  
V.K. Sundararaman ◽  
Zachariah C. Alex
Keyword(s):  
Brain Mri ◽  
Neonatal Brain ◽  
Mri Segmentation

Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain

2014 ◽  
Vol 33 (9) ◽  
pp. 1818-1831 ◽  
Author(s):  
Antonios Makropoulos ◽  
Ioannis S. Gousias ◽  
Christian Ledig ◽  
Paul Aljabar ◽  
Ahmed Serag ◽  
...  
Keyword(s):  
Brain Mri ◽  
Neonatal Brain ◽  
Mri Segmentation ◽  
Whole Brain

scholarly journals A review on automatic fetal and neonatal brain MRI segmentation

NeuroImage ◽  
2018 ◽  
Vol 170 ◽  
pp. 231-248 ◽  
Author(s):  
Antonios Makropoulos ◽  
Serena J. Counsell ◽  
Daniel Rueckert
Keyword(s):  
Brain Mri ◽  
Neonatal Brain ◽  
Mri Segmentation

scholarly journals Construction of multi-region-multi-reference atlases for neonatal brain MRI segmentation

NeuroImage ◽  
2010 ◽  
Vol 51 (2) ◽  
pp. 684-693 ◽  
Author(s):  
Feng Shi ◽  
Pew-Thian Yap ◽  
Yong Fan ◽  
John H. Gilmore ◽  
Weili Lin ◽  
...  
Keyword(s):  
Brain Mri ◽  
Neonatal Brain ◽  
Mri Segmentation

IAS‐NET: Joint Intra‐classly Adaptive GAN and Segmentation network for unsupervised cross‐domain in Neonatal Brain MRI segmentation

2021 ◽  
Author(s):  
Bo Li ◽  
Xinge You ◽  
Jing Wang ◽  
Qinmu Peng ◽  
Shi Yin ◽  
...  
Keyword(s):  
Brain Mri ◽  
Neonatal Brain ◽  
Mri Segmentation ◽  
Cross Domain

scholarly journals Neonatal brain MRI segmentation by building multi-region-multi-reference atlases

2010 ◽  
Author(s):  
Feng Shi ◽  
Pew-Thian Yap ◽  
Yong Fan ◽  
John H. Gilmore ◽  
Weili Lin ◽  
...  
Keyword(s):  
Brain Mri ◽  
Neonatal Brain ◽  
Mri Segmentation

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Quality-driven Deep Active Learning Method for 3D Brain MRI Segmentation

2021 ◽  
Author(s):  
Zhenxi Zhang ◽  
Jie Li ◽  
Chunna Tian ◽  
Zhusi Zhong ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Active Learning ◽  
Brain Mri ◽  
Learning Method ◽  
Mri Segmentation ◽  
Active Learning Method
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