scholarly journals Dual-Layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images

Author(s):  
Minjeong Kim ◽  
Guorong Wu ◽  
Isrem Rekik ◽  
Dinggang Shen
2017 ◽  
Vol 62 (17) ◽  
pp. 6853-6868 ◽  
Author(s):  
Zhenyu Tang ◽  
Yihong Wu ◽  
Yong Fan

Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan

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.


2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  

2021 ◽  
pp. 135245852098863
Author(s):  
Frank Dahlke ◽  
Douglas L Arnold ◽  
Piet Aarden ◽  
Habib Ganjgahi ◽  
Dieter A Häring ◽  
...  

Background: The Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set. Objective: The objective of this study is to describe the Novartis–Oxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes. Methods: We report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patients’ baseline age, using phase III study data (≈8000 patients). Results: NO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2 years while risk is higher (25%–75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment. Conclusion: NO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity.


Sign in / Sign up

Export Citation Format

Share Document