FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313458624> ?p ?o ?g. }

• W4313458624 abstract "Abstract Electron microscopy (EM) images of axons and their ensheathing myelin from both the central and peripheral nervous system are used for assessing myelin formation, degeneration (demyelination) and regeneration (remyelination). The g-ratio is the gold standard measure of assessing myelin thickness and quality, and traditionally is determined from measurements done manually from EM images – a time-consuming endeavour with limited reproducibility. These measurements have also historically neglected the innermost uncompacted myelin sheath, known as the inner myelin tongue. Nonetheless, the inner tongue has been shown to be important for myelin growth and some studies have reported that certain conditions can elicit its enlargement. Ignoring this fact may bias the standard g-ratio analysis, whereas quantifying the uncompacted myelin has the potential to provide novel insights in the myelin field. In this regard, we have developed AimSeg, a bioimage analysis tool for axon, inner tongue and myelin segmentation. Aided by machine learning classifiers trained on tissue undergoing remyelination, AimSeg can be used either as an automated workflow or as a user-assisted segmentation tool. Validation results show good performance segmenting all three fibre components, with the assisted segmentation showing the potential for further improvement with minimal user intervention. This results in a considerable reduction in time for analysis compared with manual annotation. AimSeg could also be used to build larger, high quality ground truth datasets to train novel deep learning models. Implemented in Fiji, AimSeg can use machine learning classifiers trained in ilastik. This, combined with a user-friendly interface and the ability to quantify uncompacted myelin, makes AimSeg a unique tool to assess myelin growth. Author Summary Myelin is formed by specialised cells that wrap themselves around axons and has a major role in the function, protection, and maintenance of nerves. These functions are disturbed by demyelinating diseases, such as multiple sclerosis. In this work we present AimSeg, a new tool based on artificial intelligence algorithms (machine learning) to assess myelin growth on electron microscopy images. Whereas standard metrics and previous computational methods focus on quantifying compact myelin, AimSeg also quantifies the inner myelin tongue (uncompacted myelin). This structure has been largely overlooked despite the fact that it has an important role in the process of myelin growth (both during development and in the adult brain) and recent studies have reported morphological changes associated with some diseases. We report the performance of AimSeg, both as a fully automated approach and in an assisted segmentation workflow that enables the user to curate the results on-the-fly while reducing human intervention to the minimum. Therefore, AimSeg stands as a novel bioimage analysis tool that meets the challenges of assessing myelin growth by supporting both standard metrics for myelin evaluation and the quantification of the uncompacted myelin in different conditions." @default.
• W4313458624 created "2023-01-06" @default.
• W4313458624 creator A5001140583 @default.
• W4313458624 creator A5024414255 @default.
• W4313458624 creator A5055039884 @default.
• W4313458624 creator A5062316592 @default.
• W4313458624 creator A5074660400 @default.
• W4313458624 creator A5082640522 @default.
• W4313458624 date "2023-01-03" @default.
• W4313458624 modified "2023-10-18" @default.
• W4313458624 title "AimSeg: a machine-learning-aided tool for axon, inner tongue and myelin segmentation" @default.
• W4313458624 cites W1789330717 @default.
• W4313458624 cites W1938046568 @default.
• W4313458624 cites W1970827634 @default.
• W4313458624 cites W1975778063 @default.
• W4313458624 cites W1976273488 @default.
• W4313458624 cites W1985808169 @default.
• W4313458624 cites W1988294221 @default.
• W4313458624 cites W2000485285 @default.
• W4313458624 cites W2004750343 @default.
• W4313458624 cites W2007499637 @default.
• W4313458624 cites W2017966457 @default.
• W4313458624 cites W2039677191 @default.
• W4313458624 cites W2040085463 @default.
• W4313458624 cites W2041083111 @default.
• W4313458624 cites W2050402365 @default.
• W4313458624 cites W2057413482 @default.
• W4313458624 cites W2059667544 @default.
• W4313458624 cites W2072350275 @default.
• W4313458624 cites W2079314684 @default.
• W4313458624 cites W2081771004 @default.
• W4313458624 cites W2087815206 @default.
• W4313458624 cites W2115585096 @default.
• W4313458624 cites W2135257877 @default.
• W4313458624 cites W2151337518 @default.
• W4313458624 cites W2152492028 @default.
• W4313458624 cites W2167279371 @default.
• W4313458624 cites W2167776784 @default.
• W4313458624 cites W2171261865 @default.
• W4313458624 cites W2262612887 @default.
• W4313458624 cites W2469475200 @default.
• W4313458624 cites W2568303975 @default.
• W4313458624 cites W2770653478 @default.
• W4313458624 cites W2785315957 @default.
• W4313458624 cites W2795091386 @default.
• W4313458624 cites W2903257891 @default.
• W4313458624 cites W291418382 @default.
• W4313458624 cites W2917576966 @default.
• W4313458624 cites W2919920117 @default.
• W4313458624 cites W2941410838 @default.
• W4313458624 cites W2961912654 @default.
• W4313458624 cites W2969269965 @default.
• W4313458624 cites W2975634117 @default.
• W4313458624 cites W2982344168 @default.
• W4313458624 cites W3146664380 @default.
• W4313458624 cites W3191154272 @default.
• W4313458624 cites W4214834277 @default.
• W4313458624 cites W4308361273 @default.
• W4313458624 doi "https://doi.org/10.1101/2023.01.02.522533" @default.
• W4313458624 hasPublicationYear "2023" @default.
• W4313458624 type Work @default.
• W4313458624 citedByCount "1" @default.
• W4313458624 countsByYear W43134586242023 @default.
• W4313458624 crossrefType "posted-content" @default.
• W4313458624 hasAuthorship W4313458624A5001140583 @default.
• W4313458624 hasAuthorship W4313458624A5024414255 @default.
• W4313458624 hasAuthorship W4313458624A5055039884 @default.
• W4313458624 hasAuthorship W4313458624A5062316592 @default.
• W4313458624 hasAuthorship W4313458624A5074660400 @default.
• W4313458624 hasAuthorship W4313458624A5082640522 @default.
• W4313458624 hasBestOaLocation W43134586241 @default.
• W4313458624 hasConcept C153180895 @default.
• W4313458624 hasConcept C154945302 @default.
• W4313458624 hasConcept C169760540 @default.
• W4313458624 hasConcept C2777619251 @default.
• W4313458624 hasConcept C2778609137 @default.
• W4313458624 hasConcept C2779530196 @default.
• W4313458624 hasConcept C41008148 @default.
• W4313458624 hasConcept C529278444 @default.
• W4313458624 hasConcept C86803240 @default.
• W4313458624 hasConcept C89600930 @default.
• W4313458624 hasConceptScore W4313458624C153180895 @default.
• W4313458624 hasConceptScore W4313458624C154945302 @default.
• W4313458624 hasConceptScore W4313458624C169760540 @default.
• W4313458624 hasConceptScore W4313458624C2777619251 @default.
• W4313458624 hasConceptScore W4313458624C2778609137 @default.
• W4313458624 hasConceptScore W4313458624C2779530196 @default.
• W4313458624 hasConceptScore W4313458624C41008148 @default.
• W4313458624 hasConceptScore W4313458624C529278444 @default.
• W4313458624 hasConceptScore W4313458624C86803240 @default.
• W4313458624 hasConceptScore W4313458624C89600930 @default.
• W4313458624 hasLocation W43134586241 @default.
• W4313458624 hasOpenAccess W4313458624 @default.
• W4313458624 hasPrimaryLocation W43134586241 @default.
• W4313458624 hasRelatedWork W2007230284 @default.
• W4313458624 hasRelatedWork W2061068109 @default.
• W4313458624 hasRelatedWork W2091618285 @default.
• W4313458624 hasRelatedWork W2168121051 @default.
• W4313458624 hasRelatedWork W2518879373 @default.
• W4313458624 hasRelatedWork W2990277388 @default.

• next