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BACKGROUND: The glenoid track concept is used to determine preoperatively whether a Hill-Sachs defect is engaging or not. Currently, the glenoid track concept relies on measurements of bony structures as well as on the confines and elasticity of the rotator cuff as a reference point, which varies extensively among individuals and therefore limits the reliability and accuracy of this concept. PURPOSE: To evaluate the reliability of the global track concept, which determines the angular distance of the Hill-Sachs defect from the center of the articular surface of the humeral head as a new reference point with the help of an automated image analysis software and 3-dimensional analysis of the humeral head. STUDY DESIGN: Controlled laboratory study. METHODS: Computed tomography scans of 100 patients treated for anterior shoulder instability with different sizes of Hill-Sachs defects were evaluated manually by 2 orthopaedic surgeons independently using the software OsiriX as well as automatically by using a dedicated prototype software (ImFusion). Obtained manual and automated measurements included the Hill-Sachs length, Hill-Sachs width, and Hill-Sachs depth of the defect; the Hill-Sachs interval (HSI); and the glenoid width for the glenoid track concept, as well as the angular distance of the Hill-Sachs defect from the center of the articular surface of the humeral head (global track concept). The reliability of the different measurement techniques was compared by calculating intraclass correlation coefficients (ICCs). RESULTS: There was a significant difference for all obtained parameters comparing manual and automatic measurements. For manually obtained parameters, measurements referring to bony boundaries (glenoid width, Hill-Sachs length, and Hill-Sachs width) showed good to excellent agreement (ICC, 0.86, 0.82, and 0.62, respectively), while measurements referring to soft tissue boundaries (HSI and glenoid track; ICC, 0.56 and 0.53, respectively) or not directly identifiable reference points (center of articular surface and global track) only showed fair reliability (ICC middle excursion, 0.42). When the same parameters were measured with the help of an automated software, good reliability for the glenoid track concept and excellent reliability for the global track concept in the middle excursion were achieved. CONCLUSION: The present study showed that the more complex global track measurements of humeral defects are more reliable than the current standard HSI and glenoid track measurements. However, this is only true when automated software is used to perform the measurements. CLINICAL RELEVANCE: Future studies using the new proposed method in combination with an automated software need to be conducted to determine critical threshold values for defects prone to engagement.

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Background: The presence of glenoid bone defects is indicative in the choice of treatment for patients with anterior shoulder instability. In contrast to traditional linear- and area-based measurements, techniques such as the consideration of glenoid concavity have been proposed and validated. Purpose: To compare the reliability of linear (1-dimensional [1D]), area (2-dimensional [2D]), and concavity (3-dimensional [3D]) measurements to quantify glenoid bone loss performed manually and to analyze how automated measurements affect reliability. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: Computed tomography images of 100 patients treated for anterior shoulder instability with differently sized glenoid defects were evaluated independently by 2 orthopaedic surgeons manually using conventional software (OsiriX; Pixmeo) as well as automatically with a dedicated prototype software program (ImFusion Suite; ImFusion). Parameters obtained included 1D (defect diameter, best-fit circle diameter), 2D (defect area, best-fit circle area), and 3D (bony shoulder stability ratio) measurements. Mean values and reliability as expressed by the intraclass correlation coefficient [ICC]) were compared between the manual and automated measurements. Results: When manually obtained, the measurements showed almost perfect agreement for 1D parameters (ICC = 0.83), substantial agreement for 2D parameters (ICC = 0.79), and moderate agreement for the 3D parameter (ICC = 0.48). When measurements were aided by automated software, the agreement between raters was almost perfect for all parameters (ICC = 0.90 for 1D, 2D, and 3D). There was a significant difference in mean values between manually versus automatically obtained measurements for 1D, 2D, and 3D parameters (P < .001 for all). Conclusion: While more advanced measurement techniques that take glenoid concavity into account are more accurate in determining the biomechanical relevance of glenoid bone loss, our study showed that the reliability of manually performed, more complex measurements was moderate.

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Research software is continuously developed to facilitate progress and innovation in the medical field. Over time, numerous research software programs have been created, making it challenging to keep abreast of what is available. This work aims to evaluate the most frequently utilized software by the computer-assisted intervention (CAI) research community. The software assessments encompass a range of criteria, including load time, stress load, multi-tasking, extensibility and range of functionalities, user-friendliness, documentation, and technical support. A total of eight software programs were selected: 3D Slicer, Elastix, ITK-SNAP, MedInria, MeVisLab, MIPAV, and Seg3D. While none of the software was found to be perfect on all evaluation criteria, 3D Slicer and ITK-SNAP emerged with the highest rankings overall. These two software programs could frequently complement each other, as 3D Slicer has a broad and customizable range of features, while ITK-SNAP excels at performing fundamental tasks in an efficient manner. Nonetheless, each software had distinctive features that may better fit the requirements of certain research projects. This review provides valuable information to CAI researchers seeking the best-suited software to support their projects. The evaluation also offers insights for the software development teams, as it highlights areas where the software can be improved.

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In the treatment of Non-Hodgkin lymphoma (NHL), multiple therapeutic options are available. Improving outcome predictions are essential to optimize treatment. The metabolic active tumor volume (MATV) has shown to be a prognostic factor in NHL. It is usually retrieved using semi-automated thresholding methods based on standardized uptake values (SUV), calculated from 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) images. However, there is currently no consensus method for NHL. The aim of this study was to review literature on different segmentation methods used, and to evaluate selected methods by using an in house created software tool. A software tool, MUltiple SUV Threshold (MUST)-segmenter was developed where tumor locations are identified by placing seed-points on the PET images, followed by subsequent region growing. Based on a literature review, 9 SUV thresholding methods were selected and MATVs were extracted. The MUST-segmenter was utilized in a cohort of 68 patients with NHL. Differences in MATVs were assessed with paired t-tests, and correlations and distributions figures. High variability and significant differences between the MATVs based on different segmentation methods (p < 0.05) were observed in the NHL patients. Median MATVs ranged from 35 to 211 cc. No consensus for determining MATV is available based on the literature. Using the MUST-segmenter with 9 selected SUV thresholding methods, we demonstrated a large and significant variation in MATVs. Identifying the most optimal segmentation method for patients with NHL is essential to further improve predictions of toxicity, response, and treatment outcomes, which can be facilitated by the MUST-segmenter.

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BACKGROUND: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. METHODS: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. RESULTS: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73-0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90-0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer "LungCTAnalyzer" and the median of the visual score (0.75 with a CI 0.67-82 and with a median value of 22% of disease extension for the software and 25% for the visual values). CONCLUSIONS: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

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Background: Considerable spinal cord (SC) atrophy occurs in multiple sclerosis (MS). While MRI-based techniques for SC cross-sectional area (CSA) quantification have improved over time, there is no common agreement on whether to measure at single vertebral levels or across larger regions and whether upper SC CSA can be reliably measured from brain images. Aim: To compare in a multicenter setting three CSA measurement methods in terms of repeatability at different anatomical levels. To analyze the agreement between measurements performed on the cervical cord and on brain MRI. Method: One healthy volunteer was scanned three times on the same day in six sites (three scanner vendors) using a 3T MRI protocol including sagittal 3D T1-weighted imaging of the brain (covering the upper cervical cord) and of the SC. Images were analyzed using two semiautomated methods [NeuroQLab (NQL) and the Active Surface Model (ASM)] and the fully automated Spinal Cord Toolbox (SCT) on different vertebral levels (C1-C2; C2/3) on SC and brain images and the entire cervical cord (C1-C7) on SC images only. Results: CSA estimates were significantly smaller using SCT compared to NQL and ASM (p < 0.001), regardless of the cord level. Inter-scanner repeatability was best in C1-C7: coefficients of variation for NQL, ASM, and SCT: 0.4, 0.6, and 1.0%, respectively. CSAs estimated in brain MRI were slightly lower than in SC MRI (all p ≤ 0.006 at the C1-C2 level). Despite protocol harmonization between the centers with regard to image resolution and use of high-contrast 3D T1-weighted sequences, the variability of CSA was partly scanner dependent probably due to differences in scanner geometry, coil design, and details of the MRI parameter settings. Conclusion: For CSA quantification, dedicated isotropic SC MRI should be acquired, which yielded best repeatability in the entire cervical cord. In the upper part of the cervical cord, use of brain MRI scans entailed only a minor loss of CSA repeatability compared to SC MRI. Due to systematic differences between scanners and the CSA quantification software, both should be kept constant within a study. The MRI dataset of this study is available publicly to test new analysis approaches.

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BACKGROUND: Gliomas are the most common malignant primary brain tumors. Assessment of the tumor volume represents a crucial point in preoperative and postoperative evaluation. OBJECTIVE: To compare pre- and postoperative tumor volumes obtained with an automated, semi-automatic, and manual segmentation tool. Mean processing time of each segmentation techniques was measured. METHODS: Manual segmentation was performed on preoperative and postoperative magnetic resonance images with the open-source software Horos (Horos Project). "SmartBrush," a tool of the IPlan Cranial software (Brainlab, Feldkirchen, Germany), was used to carry out the semi-automatic segmentation. The open-source BraTumIA software (NeuroImaging Tools and Resources Collaboratory) was employed for the automated segmentation. Pearson correlation coefficient was used to assess volumetric comparison. Subsequently deviation/range and average discrepancy were determined. The Wilcoxon signed-rank test was used to assess statistical significance. RESULTS: A total of 58 patients with a newly diagnosed high-grade glioma were enrolled. The comparison of the volumes calculated with Horos and IPlan showed a strong agreement both on preoperative and postoperative images (respectively: "enhancing" ρ = 0.99-0.78, "fluid-attenuated inversion recovery" ρ = 0.97-0.92, and "total tumor volume" ρ = 0.98-0.95). Agreement between BraTumIA and the other 2 techniques appeared to be strong for preoperative images, but showed a higher disagreement on postoperative images. Mean time expenditure for tumor segmentation was 27 min with manual segmentation, 17 min with semi-automated, and 8 min with automated software. CONCLUSION: The considered segmentation tools showed high agreement in preoperative volumetric assessment. Both manual and semi-automated software appear adequate for the postoperative quantification of residual volume. The evaluated automated software is not yet reliable. Automated software considerably reduces the time expenditure.

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Purpose: The aims of this study were to evaluate a semi-automatic segmentation software for assessment of ablation zone geometry in computed tomography (CT)-guided microwave ablation (MWA) of liver tumors and to compare two different MWA systems.Material and Methods: 27 patients with 40 hepatic tumors (primary liver tumor n = 20, metastases n = 20) referred for CT-guided MWA were included in this retrospective IRB-approved study. MWA was performed using two systems (system 1: 915 MHz; n = 20; system 2: 2.45 GHz; n = 20). Ablation zone segmentation and ellipticity index calculations were performed using SAFIR (Software Assistant for Interventional Radiology). To validate semi-automatic software calculations, results (2 perpendicular diameters, ellipticity index, volume) were compared with those of manual analysis (intraclass correlation, Pearson's correlation, Mann-Whitney U test; p < 0.05 deemed significant.Results: Manual measurements of mean maximum ablation zone diameters were 43 mm (system 1) and 34 mm (system 2), respectively. Correlations between manual and semi-automatic measurements were r = 0.72 and r = 0.66 (both p < 0.0001) for perpendicular diameters, and r = 0.98 (p < 0.001) for volume. Manual analysis demonstrated that ablation zones created with system 2 had a significantly lower ellipticity index compared to system 1 (mean 1.17 vs. 1.86, p < 0.0001). Results correlated significantly with semi-automatic software measurements (r = 0.71, p < 0.0001).Conclusion: Semi-automatic assessment of ablation zone geometry using SAFIR is feasible. Software-assisted evaluation of ablation zones may prove beneficial with complex ablation procedures, especially for less experienced operators. The 2.45 GHz MWA system generated a significantly more spherical ablation zone compared to the 915 MHz system. The choice of a specific MWA system significantly influences ablation zone geometry.

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Brain volumes computed from magnetic resonance images have potential for assisting with the diagnosis of individual dementia patients, provided that they have low measurement error and high reliability. In this paper we describe and validate icobrain dm, an automatic tool that segments brain structures that are relevant for differential diagnosis of dementia, such as the hippocampi and cerebral lobes. Experiments were conducted in comparison to the widely used FreeSurfer software. The hippocampus segmentations were compared against manual segmentations, with significantly higher Dice coefficients obtained with icobrain dm (25-75th quantiles: 0.86-0.88) than with FreeSurfer (25-75th quantiles: 0.80-0.83). Other brain structures were also compared against manual delineations, with icobrain dm showing lower volumetric errors overall. Test-retest experiments show that the precision of all measurements is higher for icobrain dm than for FreeSurfer except for the parietal cortex volume. Finally, when comparing volumes obtained from Alzheimer's disease patients against age-matched healthy controls, all measures achieved high diagnostic performance levels when discriminating patients from cognitively healthy controls, with the temporal cortex volume measured by icobrain dm reaching the highest diagnostic performance level (area under the receiver operating characteristic curve = 0.99) in this dataset.

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Patient-specific 3D modeling is the first step towards image-guided surgery, the actual revolution in surgical care. Pediatric and adolescent patients with rare tumors and malformations should highly benefit from these latest technological innovations, allowing personalized tailored surgery. This study focused on the pelvic region, located at the crossroads of the urinary, digestive, and genital channels with important vascular and nervous structures. The aim of this study was to evaluate the performances of different software tools to obtain patient-specific 3D models, through segmentation of magnetic resonance images (MRI), the reference for pediatric pelvis examination. Twelve software tools freely available on the Internet and two commercial software tools were evaluated using T2-w MRI and diffusion-weighted MRI images. The software tools were rated according to eight criteria, evaluated by three different users: automatization degree, segmentation time, usability, 3D visualization, presence of image registration tools, tractography tools, supported OS, and potential extension (i.e., plugins). A ranking of software tools for 3D modeling of MRI medical images, according to the set of predefined criteria, was given. This ranking allowed us to elaborate guidelines for the choice of software tools for pelvic surgical planning in pediatric patients. The best-ranked software tools were Myrian Studio, ITK-SNAP, and 3D Slicer, the latter being especially appropriate if nerve fibers should be included in the 3D patient model. To conclude, this study proposed a comprehensive review of software tools for 3D modeling of the pelvis according to a set of eight criteria and delivered specific conclusions for pediatric and adolescent patients that can be directly applied to clinical practice.

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PURPOSE: 3D Magnetic resonance imaging (MRI) scans are generally used for quantitative cartilage measurements in knee osteoarthritis. However, a great deal of MRI data is from 2D scans, often thought to be unsuitable for quantitative cartilage assessment. The goal of our study was to demonstrate that mLACS, a modified version of the Local Area Cartilage Segmentation (LACS) method, could be used to measure cartilage volume on 2D MRI images. METHODS: We studied 301 randomly selected subjects from the OA Biomarkers Consortium FNIH Study, a nested case-control study within the Osteoarthritis Initiative (OAI). The study comprised four subgroups based on radiographic and pain progression. We compared mLACS applied to 2D TSE scans to LACS on 3D DESS data. The Pearson's correlation coefficient was used to establish agreement between LACS and mLACS, standardized response means (SRMs) for responsiveness, and intra-class correlation coefficients (ICCs) to measure reader precision. Logistic regression in a case/control analysis was used to compare the clinical validity between the two methods. RESULTS: We found R2 = 0.76 for the correlation between LACS and mLACs. For LACS, the responsiveness was SRM = 0.49 compared to 0.39 for mLACS. The odds ratios (OR) for the primary case/control analyses were 1.62 for LACS and 1.78 for mLACS. The intra and inter reader reproducibility values for mLACS were ICC = 0.90 and 0.86, respectively. CONCLUSION: This study has demonstrated that a reproducible, responsive, and clinically valid quantitative measurement of cartilage volume can be made using 2D TSE scans with a modest loss of responsiveness compared to 3D scans.

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OBJECTIVE: Large studies of knee osteoarthritis (KOA) require well-characterized efficient methods to assess progression. We previously developed the local-area cartilage segmentation (LACS) software method, to measure cartilage volume on magnetic resonance imaging (MRI) scans. The present study further validates this method in a larger patient cohort and assesses predictive validity in a case-control study. METHOD: The OA Biomarkers Consortium FNIH Project, a case-control study of KOA progression nested within the Osteoarthritis Initiative (OAI), includes 600 subjects in four subgroups based on radiographic and pain progression. Our software tool measured change in medial femoral cartilage volume in a central weight-bearing region. Different sized regions of cartilage were assessed to explore their sensitivity to change. The readings were performed on MRI scans at the baseline and 24-month visits. We used standardized response means (SRMs) for responsiveness and logistic regression for predictive validity. RESULTS: Cartilage volume change was associated strongly with radiographic progression (odds ratios (OR) = 4.66; 95% confidence intervals (CI) = 2.85-7.62). OR were significant but of lesser magnitude for the combined radiographic and pain progression outcome (OR = 1.70; 95% CI = 1.40-2.07). For the full 600 subjects, theSRM was -0.51 for the largest segmented area. Smaller areas of cartilage segmentation were also able to predict the case-control status. The average reader time for the largest area was less than 20 min per scan. Smaller areas could be assessed with less reader time. CONCLUSION: We demonstrated that the LACS method is fast, responsive, and associated with radiographic and pain progression, and is appropriate for existing and future large studies of KOA.

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This study was aimed to evaluate the applicability of common Chinese word segmentation software used in the literature study of traditional Chinese medicine (TCM) text,in order to put forward ideas on developing specialized TCM text word segmentation software.By means of installing and operating Chinese word segmentation software,the text segmentation experiment was conducted on TCM text samples.Aspects,such Chinese word segmentation accuracy,speed,maneuverability,reliability,extendibility,portability and other characteristics,were compared among different Chinese word segmentation software.The results showed that there were differences on the accuracy,speed,maneuverability,reliability,extendibility,portability among different Chinese word segmentation software.It was difficult to achieve best performance on different aspects by single software.Through the comparison of different Chinese word segmentation software,the Pan-Gu Segment software showed the best performance on accuracy,with good maneuverability,and high word segmentation efficiency,which was the most suitable for word segmentation in TCM text.It was concluded that developing specialized TCM text segmentation software may be the best solution to meet the requirement of text segmentation in TCM literature study.Basic studies should be strengthened from aspects,such as the construction of standard TCM copus,the completion of TCM dictionary base,the introduction,optimization and innovation of word segmentation algorithm,as well as the development of word segmentation software for TCM text.

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OBJECTIVE: Magnetic resonance imaging (MRI) is a widely used imaging modality for studies of knee osteoarthritis (OA). Compared to radiography, MRI offers exceptional soft tissue imaging and true three-dimensional (3D) visualization. However, MRI is expensive both due to the cost of acquisition and evaluation of the images. The goal of our study is to develop a new method to address the cost of MRI by combining innovative acquisition methods and automated post-processing software. METHODS: Ten healthy volunteers were scanned with three different MRI protocols: A standard 3D dual-echo steady state (DESS) pulse sequence, an accelerated DESS (DESSAcc), acquired at approximately half the time compared to DESS, and a multi-echo time DESS (DESSMTE), which is capable of producing measurements of T2 relaxation time. A software tool was used to measure cartilage volume. Accuracy was quantified by comparing DESS to DESSAcc and DESSMTE and precision was measured using repeat readings and acquisitions. T2 precision was determined using duplicate DESSMTE acquisitions. Intra-class correlation coefficients (ICCs), root-mean square standard deviation (RMSSD), and the coefficient of variation (CoV) were used to quantify accuracy and precision. RESULTS: The accuracies of DESSAcc and DESSMTE were CoV = 3.7% and CoV = 6.6% respectively, while precision was 3.8%, 3.0%, and 3.1% for DESS, DESSAcc and DESSMTE. T2 repositioning precision was 5.8%. CONCLUSION: The results demonstrate that accurate and precise quantification of cartilage volume is possible using a combination of substantially faster MRI acquisition and post-processing software. Precise measurements of cartilage T2 and volume can be made using the same acquisition.

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Over the past two decades a significant number of OCT segmentation approaches have been proposed in the literature. Each methodology has been conceived for and/or evaluated using specific datasets that do not reflect the complexities of the majority of widely available retinal features observed in clinical settings. In addition, there does not exist an appropriate OCT dataset with ground truth that reflects the realities of everyday retinal features observed in clinical settings. While the need for unbiased performance evaluation of automated segmentation algorithms is obvious, the validation process of segmentation algorithms have been usually performed by comparing with manual labelings from each study and there has been a lack of common ground truth. Therefore, a performance comparison of different algorithms using the same ground truth has never been performed. This paper reviews research-oriented tools for automated segmentation of the retinal tissue on OCT images. It also evaluates and compares the performance of these software tools with a common ground truth.

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