open_iA: A tool for processing and visual analysis of industrial computed tomography datasets

Fröhler, Bernhard; Weissenböck, Johannes; Schiwarth, Marcel; Kastner, Johann; Heinzl, Christoph

doi:10.21105/joss.01185

Cited by 28 publications

(17 citation statements)

References 11 publications

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“…We used the software open_iA (Fröhler et al, 2019 ) to quantify the projected distance from the estimated landmark coordinates in Edgewarp 3D to the bone surface of the corresponding complete cranium (S1, S2, S3). The computation is based on the determination of the surface normal vector.…”

Section: Methodsmentioning

confidence: 99%

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Thickness accuracy of virtually designed patient‐specific implants for large neurocranial defects

et al. 2021

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The combination of computer‐aided design (CAD) techniques based on computed tomography (CT) data to generate patient‐specific implants is in use for decades. However, persisting disadvantages are complicated design procedures and rigid reconstruction protocols, for example, for tailored implants mimicking the patient‐specific thickness distribution of missing cranial bone. In this study we used two different approaches, CAD‐ versus thin‐plate spline (TPS)‐based implants, to reconstruct extensive unilateral and bilateral cranial defects in three clinical cases. We used CT data of three complete human crania that were virtually damaged according to the missing regions in the clinical cases. In total, we carried out 132 virtual reconstructions and quantified accuracy from the original to the generated implant and deviations in the resulting implant thickness as root‐mean‐square error (RMSE). Reconstructions using TPS showed an RMSE of 0.08–0.18 mm in relation to geometric accuracy. CAD‐based implants showed an RMSE of 0.50–1.25 mm. RMSE in relation to implant thickness was between 0.63 and 0.70 mm (TPS) while values for CAD‐based implants were significantly higher (0.63–1.67 mm). While both approaches provide implants showing a high accuracy, the TPS‐based approach additionally provides implants that accurately reproduce the patient‐specific thickness distribution of the affected cranial region.

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Section: Methodsmentioning

confidence: 99%

“…Cranial vault thickness of the complete specimens and each reconstructed cranial implant was computed based on extracted surface data (STL) in open_iA (Fröhler et al, 2019 ). Thickness values are determined for landmarks that are situated in the implant specific area.…”

Section: Methodsmentioning

confidence: 99%

Thickness accuracy of virtually designed patient‐specific implants for large neurocranial defects

et al. 2021

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“…For a volume of size 1220x854x976, a segmentation with K-means took 2.5 hours, and less than 5 minutes with Otsu thresholding. This was achieved in the open source software open_iA [19]. The results where later evaluated by material specialists to ensure and safeguard the quality of the segmentation.…”

Section: Segmentationmentioning

confidence: 99%

The long journey to the training of a deep neural network for segmenting pores and fibers

Weinberger¹,

Yosifov²,

Fröhler³

et al. 2022

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Even though it is a crucial step for achieving suitable results, the preprocessing of data before it is used as input to deep neural networks is often only described as a side note. This work elaborates on the required steps in this preprocessing procedure. Specifically, we provide insights into the selection of appropriate segmentation algorithms to generate reference volumes from X-ray computed tomography (XCT) scans as training data. Furthermore, this work evaluates the criteria for the selection of an appropriate deep learning network architecture, and a quantitative comparison between networks based on U-Net and V-Net.

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“…The methods described in this work were integrated directly into the FIAKER tool, which is implemented as a module of the open_iA framework [19]. open_iA is written in C++ and makes use of the Qt, VTK, and ITK frameworks.…”

Section: Implementation Details and Performancementioning

confidence: 99%

Analysis and comparison of algorithms for the tomographic reconstruction of curved fibres

Fröhler

Elberfeld

Möller

et al. 2020

Nondestructive Testing and Evaluation

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We present visual methods for the analysis and comparison of the results of curved fibre reconstruction algorithms, i.e., of algorithms extracting characteristics of curved fibres from X-ray computed tomography scans. In this work, we extend previous methods for the analysis and comparison of results of different fibre reconstruction algorithms or parametrisations to the analysis of curved fibres. We propose fibre dissimilarity measures for such curved fibres and apply these to compare multiple results to a specified reference. We further propose visualisation methods to analyse differences between multiple results quantitatively and qualitatively. In two case studies, we show that the presented methods provide valuable insights for advancing and parametrising fibre reconstruction algorithms, and support in improving their results in characterising curved fibres.

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open_iA: A tool for processing and visual analysis of industrial computed tomography datasets

Abstract: Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY).

Cited by 28 publications

References 11 publications

Thickness accuracy of virtually designed patient‐specific implants for large neurocranial defects

Thickness accuracy of virtually designed patient‐specific implants for large neurocranial defects

The long journey to the training of a deep neural network for segmenting pores and fibers

Analysis and comparison of algorithms for the tomographic reconstruction of curved fibres

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