Synchrotron microtomography reveals insights into the degradation kinetics of bio-degradable coronary magnesium scaffolds

Menze, Roman; Hesse, Bernhard; Kusmierczuk, Maciej; Chen, Duote; Weitkamp, Timm; Bettink, Stephanie; Scheller, Bruno

doi:10.1016/j.bioactmat.2023.09.008

Cited by 2 publications

(2 citation statements)

References 47 publications

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“…Initially, researchers relied on extensive high-quality imaging datasets to refine computer algorithms; however, in cell imaging, issues such as voluminous data and manual annotation are inevitable (e.g., CellProfiler [ 383 ] - an image analysis tool, and ilastik [ 384 ] - an image simplification tool), both requiring manual parameter adjustments for experiment-specific optimization. To address these challenges, scholars have introduced learning frameworks like U-Net [ 385 ] and neural networks to tackle multi-probe labeling (identifying numerous probe data) and the segmentation problem (cell nucleus organization identification) in cell images. In contrast, 3D spatial imaging and volume analysis of multi-dimensional tissue blocks using optical microscopy are relatively straightforward.…”

Section: Image Formationmentioning

confidence: 99%

Semiconducting polymer dots for multifunctional integrated nanomedicine carriers

Zhang,

Yu,

et al. 2024

Materials Today Bio

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Section: Image Formationmentioning

confidence: 99%

Semiconducting polymer dots for multifunctional integrated nanomedicine carriers

Zhang,

Yu,

et al. 2024

Materials Today Bio

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“…However, in some cases, this step represents a major bottleneck, as mapping the structures into labels through the image greyscales is difficult with standard automated techniques [15]. In such cases, machine and deep learning algorithms, specifically convolutional neural networks (CNN) can be employed [10,[16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A comparison of deep learning segmentation models for synchrotron radiation based tomograms of biodegradable bone implants

Lopes Marinho,

Kazimi,

Ćwieka

et al. 2024

Front. Phys.

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Introduction: Synchrotron radiation micro-computed tomography (SRμCT) has been used as a non-invasive technique to examine the microstructure and tissue integration of biodegradable bone implants. To be able to characterize parameters regarding the disintegration and osseointegration of such materials quantitatively, the three-dimensional (3D) image data provided by SRμCT needs to be processed by means of semantic segmentation. However, accurate image segmentation is challenging using traditional automated techniques. This study investigates the effectiveness of deep learning approaches for semantic segmentation of SRμCT volumes of Mg-based implants in sheep bone ex vivo.Methodology: For this purpose different convolutional neural networks (CNNs), including U-Net, HR-Net, U²-Net, from the TomoSeg framework, the Scaled U-Net framework, and 2D/3D U-Net from the nnU-Net framework were trained and validated. The image data used in this work was part of a previous study where biodegradable screws were surgically implanted in sheep tibiae and imaged using SRμCT after different healing periods. The comparative analysis of CNN models considers their performance in semantic segmentation and subsequent calculation of degradation and osseointegration parameters. The models’ performance is evaluated using the intersection over union (IoU) metric, and their generalization ability is tested on unseen datasets.Results and discussion: This work shows that the 2D nnU-Net achieves better generalization performance, with the degradation layer being the most challenging label to segment for all models.

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Synchrotron microtomography reveals insights into the degradation kinetics of bio-degradable coronary magnesium scaffolds

Cited by 2 publications

References 47 publications

Semiconducting polymer dots for multifunctional integrated nanomedicine carriers

Semiconducting polymer dots for multifunctional integrated nanomedicine carriers

A comparison of deep learning segmentation models for synchrotron radiation based tomograms of biodegradable bone implants

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