scholarly journals Visualizing polymeric bioresorbable scaffolds with three-dimensional image reconstruction using contrast-enhanced micro-computed tomography

2016 ◽  
Vol 33 (5) ◽  
pp. 731-737 ◽  
Author(s):  
Sheng Tu ◽  
Fudong Hu ◽  
Wei Cai ◽  
Liyan Xiao ◽  
Linlin Zhang ◽  
...  
Zoosymposia ◽  
2019 ◽  
Vol 15 (1) ◽  
pp. 172-191 ◽  
Author(s):  
ALEXANDER ZIEGLER

Recent studies have shown that micro-computed tomography (µCT) must be considered one of the most suitable techniques for the non-invasive, three-dimensional (3D) visualization of metazoan hard parts. In addition, µCT can also be used to visualize soft part anatomy non-destructively and in 3D. In order to achieve soft tissue contrast using µCT based on X-ray attenuation, fixed specimens must be immersed in staining solutions that include heavy metals such as silver (Ag), molybdenum (Mo), osmium (Os), lead (Pb), or tungsten (W). However, while contrast-enhancement has been successfully applied to specimens pertaining to various higher metazoan taxa, echinoderms have thus far not been analyzed using this approach. In order to demonstrate that this group of marine invertebrates is suitable for contrast-enhanced µCT as well, the present study provides results from an application of this technique to representative species from all five extant higher echinoderm taxa. To achieve soft part contrast, freshly fixed and museum specimens were immersed in an ethanol solution containing phosphotungstic acid and then scanned using a high-resolution desktop µCT system. The acquired datasets show that the combined visualization of echinoderm soft and hard parts can be readily accomplished using contrast-enhanced µCT in all extant echinoderm taxa. The results are compared with µCT data obtained using unstained specimens, with conventional histological sections, and with data previously acquired using magnetic resonance imaging, a technique known to provide excellent soft tissue contrast despite certain limitations. The suitability for 3D visualization and modeling of datasets gathered using contrast-enhanced µCT is illustrated and applications of this novel approach in echinoderm research are discussed.


2006 ◽  
Vol 34 (10) ◽  
pp. 1587-1599 ◽  
Author(s):  
Steven K. Boyd ◽  
Stephan Moser ◽  
Michael Kuhn ◽  
Robert J. Klinck ◽  
Peter L. Krauze ◽  
...  

2019 ◽  
Author(s):  
S.J.O. Rytky ◽  
A. Tiulpin ◽  
T. Frondelius ◽  
M.A.J. Finnilä ◽  
S.S. Karhula ◽  
...  

AbstractObjectiveTo develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT).DesignOsteochondral cores from 24 total knee arthroplasty patients and 2 asymptomatic cadavers (n = 34, Ø = 2 mm; n = 45, Ø = 4 mm) were imaged using CEμCT with phosphotungstic acid-staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depthwise and subjected to dimensionally reduced Local Binary Pattern-textural feature analysis. Regularized Ridge and Logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CEμCT grades (Ø = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (Ø = 4 mm samples). The performance was assessed using Spearman’s correlation, Average Precision (AP) and Area under the Receiver Operating Characteristic Curve (AUC).ResultsHighest performance on cross-validation was observed for SZ, both on Ridge regression (ρ = 0.68, p < 0.0001) and LR (AP = 0.89, AUC = 0.92). The test set evaluations yielded decreased Spearman’s correlations on all zones. For LR, performance was almost similar in SZ (AP = 0.89, AUC = 0.86), decreased in CZ (AP = 0.71→0.62, AUC = 0.77→0.63) and increased in DZ (AP = 0.50→0.83, AUC = 0.72→0.72).ConclusionWe showed that the ML-based automatic 3D histopathological grading of osteochondral samples is feasible from CEμCT. The developed method can be directly applied by OA researchers since the grading software and all source codes are publicly available.


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