Dataset Information

A New Pipeline to Automatically Segment and Semi-Automatically Measure Bone Length on 3D Models Obtained by Computed Tomography.

ABSTRACT: The characterization of developmental phenotypes often relies on the accurate linear measurement of structures that are small and require laborious preparation. This is tedious and prone to errors, especially when repeated for the multiple replicates that are required for statistical analysis, or when multiple distinct structures have to be analyzed. To address this issue, we have developed a pipeline for characterization of long-bone length using X-ray microtomography (XMT) scans. The pipeline involves semi-automated algorithms for automatic thresholding and fast interactive isolation and 3D-model generation of the main limb bones, using either the open-source ImageJ plugin BoneJ or the commercial Mimics Innovation Suite package. The tests showed the appropriate combination of scanning conditions and analysis parameters yields fast and comparable length results, highly correlated with the measurements obtained via ex vivo skeletal preparations. Moreover, since XMT is not destructive, the samples can be used afterward for histology or other applications. Our new pipelines will help developmental biologists and evolutionary researchers to achieve fast, reproducible and non-destructive length measurement of bone samples from multiple animal species.

SUBMITTER: Beltran Diaz S

PROVIDER: S-EPMC8427701 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A New Pipeline to Automatically Segment and Semi-Automatically Measure Bone Length on 3D Models Obtained by Computed Tomography.

Beltran Diaz Santiago S H'ng Chee Ho CH Qu Xinli X Doube Michael M Nguyen John Tan JT de Veer Michael M Panagiotopoulou Olga O Rosello-Diez Alberto A

Frontiers in cell and developmental biology 20210826

The characterization of developmental phenotypes often relies on the accurate linear measurement of structures that are small and require laborious preparation. This is tedious and prone to errors, especially when repeated for the multiple replicates that are required for statistical analysis, or when multiple distinct structures have to be analyzed. To address this issue, we have developed a pipeline for characterization of long-bone length using X-ray microtomography (XMT) scans. The pipeline ...[more]

PMID: 34513850

Similar Datasets

Project description:BackgroundThis study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes.MethodsPatients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations.ResultsOverall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR.ConclusionsAutomated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.

Project description:Osteoporosis is a major public health threat with significant physical, psychosocial, and financial consequences. The calcaneus bone has been used as a measurement site for risk prediction of osteoporosis by noninvasive quantitative ultrasound (QUS). By adding optical contrast to QUS, our previous studies indicate that a combination of photoacoustic (PA) and QUS, that is, PAQUS, provides a novel opportunity to assess the health of human calcaneus. Calibration of the PAQUS system is crucial to realize quantitative and repeatable measurements of the calcaneus. Therefore, a phantom which simulates the optical, ultrasound, and architectural properties of the human calcaneus, for PAQUS system calibration, is required. Additionally, a controllable phantom offers researchers a versatile framework for developing versatile structures, allowing more controlled assessment of how varying bone structures cause defined alterations in PA and QUS signals. In this work, we present the first semi-anthropomorphic calcaneus phantom for PAQUS. The phantom was developed based on nano computed-tomography (nano-CT) and stereolithography 3D printing, aiming to maximize accuracy in the approximation of both trabecular and cortical bone microstructures. Compared with the original digital input calcaneus model from a human cadaveric donor, the printed model achieved accuracies of 71.15% in total structure and 87.21% in bone volume fraction. Inorganic materials including synthetic blood, mineral oil, intralipid, and agar gel were used to model the substitutes of bone marrow and soft tissue, filling and covering the calcaneus phantom. The ultrasound and optical properties of this phantom were measured, and the results were consistent with those measured by a commercialized device and from previous in vivo studies. In addition, a short-term stability test was conducted for this phantom, demonstrating that the optical and ultrasound properties of the phantom were stable without significant variation over 1 month. This semi-anthropomorphic calcaneus phantom shows structural, ultrasound, and optical properties similar to those from a human calcaneus in vivo and, thereby, can serve as an effective source for equipment calibration and the comprehensive study of human patients.

Project description:ObjectivesPolycystic liver disease (PLD) is characterized by growth of hepatic cysts, causing hepatomegaly. Disease severity is determined using total liver volume (TLV), which can be measured from computed tomography (CT). The gold standard is manual segmentation which is time-consuming and requires expert knowledge of the anatomy. This study aims to validate the commercially available semi-automatic MMWP (Multimodality Workplace) Volume tool for CT scans of PLD patients.MethodsWe included adult patients with one (n = 60) or two (n = 46) abdominal CT scans. Semi-automatic contouring was compared with manual segmentation, using comparison of observed volumes (cross-sectional) and growth (longitudinal), correlation coefficients (CC), and Bland-Altman analyses with bias and precision, defined as the mean difference and SD from this difference. Inter- and intra-reader variability were assessed using coefficients of variation (CV) and we assessed the time to perform both procedures.ResultsMedian TLV was 5292.2 mL (IQR 3141.4-7862.2 mL) at baseline. Cross-sectional analysis showed high correlation and low bias and precision between both methods (CC 0.998, bias 1.62%, precision 2.75%). Absolute volumes were slightly higher for semi-automatic segmentation (manual 5292.2 (3141.4-7862.2) versus semi-automatic 5432.8 (3071.9-7960.2) mL, difference 2.7%, p < 0.001). Longitudinal analysis demonstrated that semi-automatic segmentation accurately measures liver growth (CC 0.908, bias 0.23%, precision 4.04%). Inter- and intra-reader variability were small (2.19% and 0.66%) and comparable to manual segmentation (1.21% and 0.63%) (p = 0.26 and p = 0.37). Semi-automatic segmentation was faster than manual tracing (19 min versus 50 min, p = 0.009).ConclusionsSemi-automatic liver segmentation is a fast and accurate method to determine TLV and liver growth in PLD patients.Key points• Semi-automatic liver segmentation using the commercially available MMWP volume tool accurately determines total liver volume as well as liver growth over time in polycystic liver disease patients. • This method is considerably faster than manual segmentation through the use of Hounsfield unit settings. • We used a real-life CT set for the validation and showed that the semi-automatic tool measures accurately regardless of contrast used for the CT scan or not, presence of polycystic kidneys, liver volume, and previous invasive treatment for polycystic liver disease.

Dataset Information

A New Pipeline to Automatically Segment and Semi-Automatically Measure Bone Length on 3D Models Obtained by Computed Tomography.

Publications

A New Pipeline to Automatically Segment and Semi-Automatically Measure Bone Length on 3D Models Obtained by Computed Tomography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets