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MicroCT vascular network analysis program: Development, validation, and comparison to manufacturer software.

ABSTRACT: The dependence on angiogenesis for bone repair makes accurate measuring of vascular networks of great importance to orthopedic researchers. A three-dimensional imaging modality like microcomputed tomography (µCT) would better capture these networks than histology. There are commercially available programs to analyze vessel networks in three dimensions, but these may be too costly for laboratories. Alternatively, µCT trabecular software could be used but may not be appropriate. The goal of this project was to develop a vascular network analysis protocol based on freely or commonly available software and compare its performance to that of a µCT trabecular analysis software. The protocol developed, called vascular network analysis or VNA, relies on two modules in Fiji ImageJ and a custom MATLAB program. We validated the software and compared it to a µCT trabecular analysis program (MicroCT) using in silico models of increasing complexity and differing homogeneity. In general, VNA outcomes were significantly different from true values, but most were within an acceptable percent error (<10%). VNA and MicroCT performed almost identically for volume but significantly differently for average vessel diameter. For the homogenous models, the average diameters differed only slightly but were starkly different for the heterogeneous models. In the most heterogeneous system, the MicroCT software overestimated average diameter by about 650% from true. VNA was within 1% of true for the same model. In conclusion, we have developed a program to analyze vascular networks from MicroCT scans which is easily accessible, insensitive to network homogeneity, and of higher accuracy compared to a µCT trabecular analysis software.

SUBMITTER: Peters J

PROVIDER: S-EPMC7790441 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundManual segmentation is currently the gold standard to assess white matter hyperintensities (WMH), but it is time consuming and subject to intra and inter-operator variability.PurposeTo compare automatic methods to segment white matter hyperintensities (WMH) in the elderly in order to assist radiologist and researchers in selecting the most relevant method for application on clinical or research data.Material and methodsWe studied a research dataset composed of 147 patients, including 97 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 database and 50 patients from ADNI 3 and a clinical routine dataset comprising 60 patients referred for cognitive impairment at the Pitié-Salpêtrière hospital (imaged using four different MRI machines). We used manual segmentation as the gold standard reference. Both manual and automatic segmentations were performed using FLAIR MRI. We compared seven freely available methods that produce segmentation mask and are usable by a radiologist without a strong knowledge of computer programming: LGA (Schmidt et al., 2012), LPA (Schmidt, 2017), BIANCA (Griffanti et al., 2016), UBO detector (Jiang et al., 2018), W2MHS (Ithapu et al., 2014), nicMSlesion (with and without retraining) (Valverde et al., 2019, 2017). The primary outcome for assessing segmentation accuracy was the Dice similarity coefficient (DSC) between the manual and the automatic segmentation software. Secondary outcomes included five other metrics.ResultsA deep learning approach, NicMSlesion, retrained on data from the research dataset ADNI, performed best on this research dataset (DSC: 0.595) and its DSC was significantly higher than that of all others. However, it ranked fifth on the clinical routine dataset and its performance severely dropped on data with artifacts. On the clinical routine dataset, the three top-ranked methods were LPA, SLS and BIANCA. Their performance did not differ significantly but was significantly higher than that of other methods.ConclusionThis work provides an objective comparison of methods for WMH segmentation. Results can be used by radiologists to select a tool.

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MicroCT vascular network analysis program: Development, validation, and comparison to manufacturer software.

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