ArticleSensors (Basel, Switzerland)2022
Hyperconnected Openings Codified in a Max Tree Structure: An Application for Skull-Stripping in Brain MRI T1.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease.Brain sciences · 2023Article
- Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records.Frontiers in neuroscience · 2023Article
- Interpretation for Individual Brain Age Prediction Based on Gray Matter Volume.Brain sciences · 2022Article
- Computational Study of Magnetic Particle Motion inside the Nasal Cavity under the Impact of an External Magnetic Field for Biomedical Applications.Micromachines · 2022Article
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6 authors.
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Abstract
This article presents two procedures involving a maximal hyperconnected function and a hyperconnected lower leveling to segment the brain in a magnetic resonance imaging T1 weighted using new openings on a max-tree structure. The openings are hyperconnected and are viscous transformations. The first procedure considers finding the higher hyperconnected maximum by using an increasing criterion that plays a central role during segmentation. The second procedure utilizes hyperconnected lower leveling, which acts as a marker, controlling the reconstruction process into the mask. As a result, the proposal allows an efficient segmentation of the brain to be obtained. In total, 38 magnetic resonance T1-weighted images obtained from the Internet Brain Segmentation Repository are segmented. The Jaccard and Dice indices are computed, compared, and validated with the efficiency of the Brain Extraction Tool software and other algorithms provided in the literature.
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