Accuracy of bone segmentation and surface generation strategies analyzed by using synthetic CT volumes

Engelkes, Karolin

doi:10.1111/joa.13383

Cited by 4 publications

(6 citation statements)

References 102 publications

(150 reference statements)

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“…Therefore, we corrupted all the images with Gaussian white noise. According to the state-of-the-art [9,29,30], it is recommended to use values from 0.1 (better case) to 0.9 (worst case) of standard deviation (SD) to configure the white noise for the case of computer tomographies.…”

Section: Adding Noisementioning

confidence: 99%

“…Most of the above investigations begin with a manual examination performed by clinical experts supported by a computational algorithm. The procedure attaches to the following structure: image conditioning, feature extraction, segmentation of bone tissue (trabecular or cortical), and finally, quantifying the bone tissue and its quality [8][9][10]. From a computational point of view, there is a continuing interest in developing algorithms that outperform established algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Synthetic images can be configured according to a particular purpose. For example, adding different noise levels to raw data is helpful to test denoising algorithms [12], compare the segmentation accuracy of a set of algorithms related to bone segmentation [9], and detect microstructural features of trabecular bone [13]. Micro-CT is the medical imaging standard in scanners for acquiring trabecular bone samples because of the provided representation of the micro-structure size and image quality [8,[14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…The stereolithography approach manufactures trabecular bone models by printing 3D objects based on a layer-by-layer fabrication of structures defined by a numerical dataset [20,21]. Modifying pre-existing models by changing the voxel size, reconstruction algorithm, or adding a particular type of noise is a reliable approach for obtaining synthetic images [9]. Peña presented a novel method to generate synthetic images by combining the CT images from an anatomical model PBU-50 and a numerical algorithm [22].…”

Section: Introductionmentioning

confidence: 99%

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Generation of Synthetic Images of Trabecular Bone Based on Micro-CT Scans

Grande-Barreto,

Polanco-Castro,

Peregrina-Barreto

et al. 2023

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Creating synthetic images of trabecular tissue provides an alternative for researchers to validate algorithms designed to study trabecular bone. Developing synthetic images requires baseline data, such as datasets of digital biological samples or templates, often unavailable due to privacy restrictions. Even when this baseline is available, the standard procedure combines the information to generate a single template as a starting point, reducing the variability in the generated synthetic images. This work proposes a methodology for building synthetic images of trabecular bone structure, creating a 3D network that simulates it. Next, the technical characteristics of the micro-CT scanner, the biomechanical properties of trabecular bones, and the physics of the imaging process to produce a synthetic image are simulated. The proposed methodology does not require biological samples, datasets, or templates to generate synthetic images. Since each synthetic image built is unique, the methodology is enabled to generate a vast number of synthetic images, useful in the performance comparison of algorithms under different imaging conditions. The created synthetic images were assessed using microarchitecture parameters of reference, and experimental results provided evidence that the obtained values match approaches requiring initial data. The scope of this methodology covers research aspects related to using synthetic images in further biomedical research or the development of educational training tools to understand the medical image.

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Section: Adding Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generation of Synthetic Images of Trabecular Bone Based on Micro-CT Scans

Grande-Barreto,

Polanco-Castro,

Peregrina-Barreto

et al. 2023

Information

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show abstract

“…Thus, this methodology may facilitate additional assessments in complex anatomical environments [ 16 , 19 ]. The use of 3D mesh model analysis has been studied in both cardiology and other fields such as orthopedics, dentistry, and others [ 20 , 21 , 22 ]. The current study was designed to examine the feasibility of semiautomated VR volumetric analysis for the accurate measurement of cardiac chamber volumes.…”

Section: Introductionmentioning

confidence: 99%

Proof of concept: Comparative accuracy of semiautomated VR modeling for volumetric analysis of the heart ventricles

Yogev¹,

Tejman‐Yarden²,

Feinberg³

et al. 2022

Heliyon

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BounTI (boundary‐preserving threshold iteration): A user‐friendly tool for automatic hard tissue segmentation

Didziokas,

Pauws,

Kölby

et al. 2024

Journal of Anatomy

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X‐ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub‐parts and can be a time‐consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open‐access and user‐friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High‐resolution microCT images resulted in more accurate boundary‐preserved segmentation, nonetheless significantly lower‐quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add‐on, a stand‐alone Windows executable, and a Python library. We believe this accessible and user‐friendly segmentation tool can benefit the wider anatomical community.

show abstract

Accuracy of bone segmentation and surface generation strategies analyzed by using synthetic CT volumes

Cited by 4 publications

References 102 publications

Generation of Synthetic Images of Trabecular Bone Based on Micro-CT Scans

Generation of Synthetic Images of Trabecular Bone Based on Micro-CT Scans

Proof of concept: Comparative accuracy of semiautomated VR modeling for volumetric analysis of the heart ventricles

BounTI (boundary‐preserving threshold iteration): A user‐friendly tool for automatic hard tissue segmentation

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