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Domain Generalization and Multidimensional Approach for Brain MRI Segmentation Using Contrastive Representation Transfer Learning Algorithm
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Author(s): S. S. Subashka Ramesh (SRM Institute of Science and Technology, Ramapuram, India), Anish Jose (SRM Institute of Science and Technology, Ramapuram, India), P. R. Samraysh (SRM Institute of Science and Technology, Ramapuram, India), Harshavardhan Mulabagala (SRM Institute of Science and Technology, Ramapuram, India), M. S. Minu (SRM Institute of Science and Technology, Ramapuram, India)and Varun Kumar Nomula (Georgia Insitute of Technology, USA)
Copyright: 2024
Pages: 17
Source title:
Advancements in Clinical Medicine
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Instıtute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-5946-4.ch002
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Abstract
Quantitative examination of human brain development when the individual is still in the womb is essential for aberrant characterisation. Therefore, the segmentation of magnetic resonance images (MRI) is a valuable asset for quantitative analysis. Conversely, there is little variation within cohorts of foetal brain MRI annotated datasets, which makes it difficult to create automatic segmentation methods. Within this framework, the authors suggest harnessing the potential of foetal brain MRI super-resolution (SR) reconstruction techniques to produce several re-creations of a one subject with varied parameters. As a data augmentation strategy, this would work well without requiring any tuning. In general, the latter makes a major improvement to the generalisation of segmentation approaches over pipelines. When it comes to the diagnosis and treatment of neurological illnesses, the accurate segmentation of brain magnetic resonance imaging (MRI) is absolutely necessary.
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