A fibre tracking algorithm for volumetric microstructural data - application to tendons

Raymond-Hayling, Helena; Lu, Yinhui; Kadler, Karl E.; Shearer, Tom

doi:10.1016/j.actbio.2022.10.043

Cited by 3 publications

(1 citation statement)

References 71 publications

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“…This has allowed us to measure quantities such as fibril diameter, circularity and centre-to-centre distances, but it did not allow us to measure the evolution of three-dimensional features such as the fibril crimp distribution. In a recent paper, we developed an algorithm for automated tracking of fibrils in three dimensions, using serial block face-scanning electron microscopy data as the input [29] . Whilst collecting such data for the number of time points that we have considered in this study would be expensive, it would be valuable in the future to do so to allow for such analysis.…”

Section: Discussionmentioning

confidence: 99%

A preliminary study into the emergence of tendon microstructure during postnatal development

Raymond-Hayling,

Lu,

Shearer

et al. 2024

Matrix Biology Plus

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Section: Discussionmentioning

confidence: 99%

A preliminary study into the emergence of tendon microstructure during postnatal development

Raymond-Hayling,

Lu,

Shearer

et al. 2024

Matrix Biology Plus

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Neural network auto‐segmentation of serial‐block‐face scanning electron microscopy images exhibit collagen fibril structural differences with tendon type and health

Bloom,

Sabanayagam,

Benson

et al. 2024

Journal Orthopaedic Research

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A U‐Net machine learning algorithm was adapted to automatically segment tendon collagen fibril cross‐sections from serial block face scanning electron microscopy (SBF‐SEM) and create three‐dimensional (3D) renderings. We compared the performance of routine Otsu thresholding and U‐Net for a positional tendon that has low fibril density (rat tail tendon), an energy‐storing tendon that has high fibril density (rat plantaris tendon), and a high fibril density tendon hypothesized to have disorganized 3D ultrastructure (degenerated rat plantaris tendon). The area segmentation of the tail and healthy plantaris tendon had excellent accuracy for both the Otsu and U‐Net, with an Intersection over Union (IoU) of 0.8. With degeneration, only the U‐Net could accurately segment the area, whereas Otsu IoU was only 0.45. For boundary validation, the U‐Net outperformed Otsu segmentation for all tendons. The fibril diameter from U‐Net was within 10% of the manual segmentation, however, the Otsu underestimated the fibril diameter by 39% in healthy plantaris and by 84% in the degenerated plantaris. Fibril geometry was averaged across the entire image stack and compared across tendon types. The tail had a lower fibril area fraction (58%) and larger fibril diameter (0.31 µm) than the healthy plantaris (67% and 0.21 µm) and degenerated plantaris tendon (66% and 0.19 µm). This method can be applied to a large variety of tissues to quantify 3D collagen fibril structure.

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OrientationJ Algorithm for Enhancing Printed Arabic Character Analysis

Yuadi,

Nihaya,

Pratiwi

et al. 2024

2024 16th International Conference on Information Technology and Electrical Engineering (ICITEE)

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A fibre tracking algorithm for volumetric microstructural data - application to tendons

Cited by 3 publications

References 71 publications

A preliminary study into the emergence of tendon microstructure during postnatal development

A preliminary study into the emergence of tendon microstructure during postnatal development

Neural network auto‐segmentation of serial‐block‐face scanning electron microscopy images exhibit collagen fibril structural differences with tendon type and health

OrientationJ Algorithm for Enhancing Printed Arabic Character Analysis

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