A Machine Learning Algorithm to Estimate Sarcopenia on Abdominal CT.

RATIONALE AND OBJECTIVES: To assess whether a fully-automated deep learning system can accurately detect and analyze truncal musculature at multiple lumbar vertebral levels and muscle groupings on abdominal CT for potential use in the detection of central sarcopenia. MATERIALS AND METHODS: A computer system for automated segmentation of truncal musculature groups was designed and created. Abdominal CT scans of 102 sequential patients (mean age 68 years, range 59-81 years; 53 women, 49 men) conducted between January 2015 and February 2015 were assembled as a data set. Truncal musculature was manually segmented on axial CT images at multiple lumbar vertebral levels as reference standard data, divided into training and testing subsets, and analyzed by the system. Dice similarity coefficients were calculated to evaluate system performance. IRB approval was obtained, with waiver of informed consent in this retrospective study. RESULTS: System performance as gauged by the Dice coefficients, for detecting the total abdominal muscle cross-section at the level of the third and fourth lumbar vertebrae, were, respectively, 0.953 ± 0.015 and 0.953 ± 0.011 for the training set, and 0.938 ± 0.028 and 0.940 ± 0.026 for the testing set. Dice coefficients for detecting total psoas muscle cross-section at the level of the third and fourth lumbar vertebrae, were, respectively, 0.942 ± 0.040 and 0.951 ± 0.037 for the training set, and 0.939 ± 0.028 and 0.946 ± 0.032 for the testing set. CONCLUSION: This system fully-automatically and accurately segments multiple muscle groups at all lumbar spine levels on abdominal CT for detection of sarcopenia.

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift.

Artificial intelligence is upending many of our assumptions about the ability of computers to detect and diagnose diseases on medical images. Deep learning, a recent innovation in artificial intelligence, has shown the ability to interpret medical images with sensitivities and specificities at or near that of skilled clinicians for some applications. In this review, we summarize the history of artificial intelligence, present some recent research advances, and speculate about the potential revolutionary clinical impact of the latest computer techniques for bone and muscle imaging. © 2019 American Society for Bone and Mineral Research. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

Deep learning-based muscle segmentation and quantification at abdominal CT: application to a longitudinal adult screening cohort for sarcopenia assessment.

OBJECTIVE: To investigate a fully automated abdominal CT-based muscle tool in a large adult screening population. METHODS: A fully automated validated muscle segmentation algorithm was applied to 9310 non-contrast CT scans, including a primary screening cohort of 8037 consecutive asymptomatic adults (mean age, 57.1±7.8 years; 3555M/4482F). Sequential follow-up scans were available in a subset of 1171 individuals (mean interval, 5.1 years). Muscle tissue cross-sectional area and attenuation (Hounsfield unit, HU) at the L3 level were assessed, including change over time. RESULTS: Mean values were significantly higher in males for both muscle area (190.6±33.6 vs 133.3±24.1 cm2, p<0.001) and density (34.3±11.1 HU vs 27.3±11.7 HU, p<0.001). Age-related losses were observed, with mean muscle area reduction of -1.5 cm2/year and attenuation reduction of -1.5 HU/year. Overall age-related muscle density (attenuation) loss was steeper than for muscle area for both sexes up to the age of 70 years. Between ages 50 and 70, relative muscle attenuation decreased significantly more in females (-30.6% vs -18.0%, p<0.001), whereas relative rates of muscle area loss were similar (-8%). Between ages 70 and 90, males lost more density (-22.4% vs -7.5%) and area (-13.4% vs -6.9%, p<0.001). Of the 1171 patients with longitudinal follow-up, 1013 (86.5%) showed a decrease in muscle attenuation, 739 (63.1%) showed a decrease in area, and 1119 (95.6%) showed a decrease in at least one of these measures. CONCLUSION: This fully automated CT muscle tool allows for both individualized and population-based assessment. Such data could be automatically derived at abdominal CT regardless of study indication, allowing for opportunistic sarcopenia detection. ADVANCES IN KNOWLEDGE: This fully automated tool can be applied to routine abdominal CT scans for prospective or retrospective opportunistic sarcopenia assessment, regardless of the original clinical indication. Mean values were significantly higher in males for both muscle area and muscle density. Overall age-related muscle density (attenuation) loss was steeper than for muscle area for both sexes, and therefore may be a more valuable predictor of adverse outcomes.

Novel Focus of Sin Nombre Virus in Peromyscus eremicus Mice, Death Valley National Park, California, USA.

The deer mouse (Peromyscus maniculatus) is the primary reservoir for Sin Nombre virus (SNV) in the western United States. Rodent surveillance for hantavirus in Death Valley National Park, California, USA, revealed cactus mice (P. eremicus) as a possible focal reservoir for SNV in this location. We identified SNV antibodies in 40% of cactus mice sampled.

MOLECULAR SURVEILLANCE FOR BARTONELLA, BORRELIA, AND RICKETTSIA SPECIES IN TICKS FROM DESERT BIGHORN SHEEP ( OVIS CANADENSIS) AND MULE DEER ( ODOCOILEUS HEMIONUS) IN SOUTHERN CALIFORNIA, USA.

: Ticks (Acari: Ixodidae) were collected from 44 desert bighorn sheep ( Ovis canadensis) and 10 mule deer ( Odocoileus hemionus) in southern California, US during health inspections in 2015-16. Specimens were identified and screened by PCR analysis to determine the presence and prevalence of Bartonella, Borrelia, and Rickettsia species in ticks associated with these wild ruminants. None of the 60 Dermacentor hunteri and 15 Dermacentor albipictus ticks tested yielded positive PCR results. Additional tick specimens should be collected and tested to determine the prevalence of these confirmed or suspected tickborne pathogens within ruminant populations.

Mixed spine metastasis detection through positron emission tomography/computed tomography synthesis and multiclassifier.

Bone metastases are a frequent occurrence with cancer, and early detection can guide the patient's treatment regimen. Metastatic bone disease can present in density extremes as sclerotic (high density) and lytic (low density) or in a continuum with an admixture of both sclerotic and lytic components. We design a framework to detect and characterize the varying spectrum of presentation of spine metastasis on positron emission tomography/computed tomography (PET/CT) data. A technique is proposed to synthesize CT and PET images to enhance the lesion appearance for computer detection. A combination of watershed, graph cut, and level set algorithms is first run to obtain the initial detections. Detections are then sent to multiple classifiers for sclerotic, lytic, and mixed lesions. The system was tested on 44 cases with 225 sclerotic, 139 lytic, and 92 mixed lesions. The results showed that sensitivity (false positive per patient) was 0.81 (2.1), 0.81 (1.3), and 0.76 (2.1) for sclerotic, lytic, and mixed lesions, respectively. It also demonstrates that using PET/CT data significantly improves the computer aided detection performance over using CT alone.

Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images.

Purpose To create and validate a computer system with which to detect, localize, and classify compression fractures and measure bone density of thoracic and lumbar vertebral bodies on computed tomographic (CT) images. Materials and Methods Institutional review board approval was obtained, and informed consent was waived in this HIPAA-compliant retrospective study. A CT study set of 150 patients (mean age, 73 years; age range, 55-96 years; 92 women, 58 men) with (n = 75) and without (n = 75) compression fractures was assembled. All case patients were age and sex matched with control subjects. A total of 210 thoracic and lumbar vertebrae showed compression fractures and were electronically marked and classified by a radiologist. Prototype fully automated spinal segmentation and fracture detection software were then used to analyze the study set. System performance was evaluated with free-response receiver operating characteristic analysis. Results Sensitivity for detection or localization of compression fractures was 95.7% (201 of 210; 95% confidence interval [CI]: 87.0%, 98.9%), with a false-positive rate of 0.29 per patient. Additionally, sensitivity was 98.7% and specificity was 77.3% at case-based receiver operating characteristic curve analysis. Accuracy for classification by Genant type (anterior, middle, or posterior height loss) was 0.95 (107 of 113; 95% CI: 0.89, 0.98), with weighted κ of 0.90 (95% CI: 0.81, 0.99). Accuracy for categorization by Genant height loss grade was 0.68 (77 of 113; 95% CI: 0.59, 0.76), with a weighted κ of 0.59 (95% CI: 0.47, 0.71). The average bone attenuation for T12-L4 vertebrae was 146 HU ± 29 (standard deviation) in case patients and 173 HU ± 42 in control patients; this difference was statistically significant (P < .001). Conclusion An automated machine learning computer system was created to detect, anatomically localize, and categorize vertebral compression fractures at high sensitivity and with a low false-positive rate, as well as to calculate vertebral bone density, on CT images. © RSNA, 2017 Online supplemental material is available for this article.

Distribution and Diversity of Bartonella washoensis Strains in Ground Squirrels from California and Their Potential Link to Human Cases.

We investigated the prevalence of Bartonella washoensis in California ground squirrels (Otospermophilus beecheyi) and their fleas from parks and campgrounds located in seven counties of California. Ninety-seven of 140 (69.3%) ground squirrels were culture positive and the infection prevalence by location ranged from 25% to 100%. In fleas, 60 of 194 (30.9%) Oropsylla montana were found to harbor Bartonella spp. when screened using citrate synthase (gltA) specific primers, whereas Bartonella DNA was not found in two other flea species, Hoplopsyllus anomalus (n = 86) and Echidnophaga gallinacea (n = 6). The prevalence of B. washoensis in O. montana by location ranged from 0% to 58.8%. A majority of the gltA sequences (92.0%) recovered from ground squirrels and fleas were closely related (similarity 99.4-100%) to one of two previously described strains isolated from human patients, B. washoensis NVH1 (myocarditis case in Nevada) and B. washoensis 08S-0475 (meningitis case in California). The results from this study support the supposition that O. beecheyi and the flea, O. montana, serve as a vertebrate reservoir and a vector, respectively, of zoonotic B. washoensis in California.

A multi-center milestone study of clinical vertebral CT segmentation.

A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention.

Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT.

PURPOSE: To design and validate a fully automated computer system for the detection and anatomic localization of traumatic thoracic and lumbar vertebral body fractures at computed tomography (CT). MATERIALS AND METHODS: This retrospective study was HIPAA compliant. Institutional review board approval was obtained, and informed consent was waived. CT examinations in 104 patients (mean age, 34.4 years; range, 14-88 years; 32 women, 72 men), consisting of 94 examinations with positive findings for fractures (59 with vertebral body fractures) and 10 control examinations (without vertebral fractures), were performed. There were 141 thoracic and lumbar vertebral body fractures in the case set. The locations of fractures were marked and classified by a radiologist according to Denis column involvement. The CT data set was divided into training and testing subsets (37 and 67 subsets, respectively) for analysis by means of prototype software for fully automated spinal segmentation and fracture detection. Free-response receiver operating characteristic analysis was performed. RESULTS: Training set sensitivity for detection and localization of fractures within each vertebra was 0.82 (28 of 34 findings; 95% confidence interval [CI]: 0.68, 0.90), with a false-positive rate of 2.5 findings per patient. The sensitivity for fracture localization to the correct vertebra was 0.88 (23 of 26 findings; 95% CI: 0.72, 0.96), with a false-positive rate of 1.3. Testing set sensitivity for the detection and localization of fractures within each vertebra was 0.81 (87 of 107 findings; 95% CI: 0.75, 0.87), with a false-positive rate of 2.7. The sensitivity for fracture localization to the correct vertebra was 0.92 (55 of 60 findings; 95% CI: 0.79, 0.94), with a false-positive rate of 1.6. The most common cause of false-positive findings was nutrient foramina (106 of 272 findings [39%]). CONCLUSION: The fully automated computer system detects and anatomically localizes vertebral body fractures in the thoracic and lumbar spine on CT images with a high sensitivity and a low false-positive rate.

Cortical shell unwrapping for vertebral body abnormality detection on computed tomography.

The vertebral body is the main axial load-bearing structure of the spinal vertebra. Assessment of acute injury and chronic deformity of the vertebral body is difficult to assess accurately and quantitatively by simple visual inspection. We propose a cortical shell unwrapping method to examine the vertebral body for injury such as fractures and degenerative osteophytes. The spine is first segmented and partitioned into vertebrae. Then the cortical shell of the vertebral body is extracted using deformable dual-surface models. The cortical shell is then unwrapped onto a 2D map and the complex 3D detection problem is effectively converted to a pattern recognition problem on a 2D plane. Characteristic features adapted for different applications are computed and sent to a committee of support vector machines for classification. The system was evaluated on two applications, one for fracture detection on trauma CT datasets and the other on degenerative osteophyte assessment on sodium fluoride PET/CT. The fracture CAD achieved 93.6% sensitivity at 3.2 false positive per patient and the degenerative osteophyte CAD achieved 82% sensitivity at 4.7 false positive per patient.

Automated detection of sclerotic metastases in the thoracolumbar spine at CT.

PURPOSE: To design and validate a computer system for automated detection and quantitative characterization of sclerotic metastases of the thoracolumbar spine on computed tomography (CT) images. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board and was HIPAA compliant; informed consent was waived. The data set consisted of CT examinations in 49 patients (14 female, 35 male patients; mean age, 57.0 years; range, 12-77 years), demonstrating a total of 532 sclerotic lesions of the spine of greater than 0.3 cm(3) in volume, and in 10 control case patients (four women, six men; mean age, 55.2 years; range, 19-70 years) without spinal lesions. CT examinations were divided into training and test sets, and images were analyzed according to prototypical fully-automated computer-aided detection (CAD) software. Free-response receiver operating characteristic analysis was performed. RESULTS: Lesion detection sensitivity on images in the training set was 90%, relative to reference-standard marked lesions (95% confidence interval [CI]: 83%, 97%), at a false-positive rate (FPR) of 10.8 per patient (95% CI: 6.6, 15.0). For images in the testing set, sensitivity was 79% (95% CI: 74%, 84%), with an FPR of 10.9 per patient (95% CI: 8.5, 13.3). False-negative findings were most commonly (37 [40%] of 93) a result of endplate proximity, with 32 (34% of 93) caused by low CT attenuation. Marginal sclerosis caused by degenerative change (174 [28.1%] of 620 actual detections) was the most common cause of false-positive detections, followed by partial volume averaging with vertebral endplates (173 [27.9%] of 620) and pedicle cortex parallel to the axial imaging plane (121 [19.5%] 620). CONCLUSION: This CAD system successfully identified and segmented sclerotic lesions in the thoracolumbar spine.

Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence.

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp ≥6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for "easy" and "moderate" polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC.

Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods.

Ability of newly emerged adult Culex quinquefasciatus (Diptera: Culicidae) mosquitoes to exit belowground stormwater treatment systems via lateral conveyance pipes.

Culex quinquefasciatus Say mosquitoes flourish in belowground stormwater systems in the southern United States. Recent evidence suggests that oviposition-site-seeking females may have difficulties locating, entering, and ovipositing inside permanent water chambers when surface entry through pickholes in manhole covers are sealed. It remains unknown, however, if newly emerged adults are able to detect cues necessary to exit these partly sealed systems via lateral conveyance pipes or if they perish belowground. Fourth instar Cx. quinquefasciatus were placed within proprietary belowground stormwater treatment systems to determine the percentage of newly emerged adults able to escape treatment chambers via a single lateral conveyance pipe. Overall, 56% of deployed mosquitoes were captured in adult exit traps with an 1:1 male:female ratio. The percentage of adults captured varied significantly among chambers, but was not associated with structural site characteristics such as the chamber depth or the length and course of conveyance pipe to the exit trap. Empirical observations suggested that longbodied cellar spiders, Pholcus phalangioides (Fuesslin), ubiquitous in these structures, may have reduced adult trap capture. Findings demonstrate that newly emerged Cx. quinquefasciatus can exit subterranean chambers under potentially difficult structural conditions but suggest that a portion may perish in the absence of surface exit points in manhole shafts.

Magnetic resonance imaging of triangular fibrocartilage.

Due to their small size and complex structure, diagnosing injury of the proximal wrist ligamentous structures can be challenging. The triangular fibrocartilage complex (TFCC) is an example of one such structure, for which lesions may be missed unless high-resolution magnetic resonance imaging (MRI) obtained via a standard matrix with a small field of view or high-resolution imaging matrix (small spatial scale matrix elements/large matrix size) is utilized. While there have been recent advances in increasing MRI spatial resolution, attempts at improved visualization by isolated increase in the spatial resolution will be ineffective if the signal-to-noise ratio (SNR) of the images obtained is low. Additionally, high contrast resolution is important to facilitate a more precise visualization of these structures and their pathology. Thus, a balance of the three important imaging factor qualifications of high spatial resolution, high SNR, and high contrast resolution must be struck for optimized TFCC and wrist imaging. The goal of this article, then, is to elucidate the theory and techniques of effective high-resolution imaging of the proximal ligamentous structures of the wrist, balancing SNR and high contrast resolution constraints, and focusing on imaging of the TFCC as a prototypical example.

Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography.

PURPOSE: To assess the diagnostic performance of distributed human intelligence for the classification of polyp candidates identified with computer-aided detection (CAD) for computed tomographic (CT) colonography. MATERIALS AND METHODS: This study was approved by the institutional Office of Human Subjects Research. The requirement for informed consent was waived for this HIPAA-compliant study. CT images from 24 patients, each with at least one polyp of 6 mm or larger, were analyzed by using CAD software to identify 268 polyp candidates. Twenty knowledge workers (KWs) from a crowdsourcing platform labeled each polyp candidate as a true or false polyp. Two trials involving 228 KWs were conducted to assess reproducibility. Performance was assessed by comparing the area under the receiver operating characteristic curve (AUC) of KWs with the AUC of CAD for polyp classification. RESULTS: The detection-level AUC for KWs was 0.845 ± 0.045 (standard error) in trial 1 and 0.855 ± 0.044 in trial 2. These were not significantly different from the AUC for CAD, which was 0.859 ± 0.043. When polyp candidates were stratified by difficulty, KWs performed better than CAD on easy detections; AUCs were 0.951 ± 0.032 in trial 1, 0.966 ± 0.027 in trial 2, and 0.877 ± 0.048 for CAD (P = .039 for trial 2). KWs who participated in both trials showed a significant improvement in performance going from trial 1 to trial 2; AUCs were 0.759 ± 0.052 in trial 1 and 0.839 ± 0.046 in trial 2 (P = .041). CONCLUSION: The performance of distributed human intelligence is not significantly different from that of CAD for colonic polyp classification.

Detection of vertebral body fractures based on cortical shell unwrapping.

Assessment of trauma patients with multiple injuries can be one of the most clinically challenging situations dealt with by the radiologist. We propose a fully automated method to detect acute vertebral body fractures on trauma CT studies. The spine is first segmented and partitioned into vertebrae. Then the cortical shell of the vertebral body is extracted using deformable dual-surface models. The extracted cortical shell is unwrapped onto a 2D map effectively converting a complex 3D fracture detection problem into a pattern recognition problem of fracture lines on a 2D plane. Twenty-eight features are computed for each fracture line and sent to a committee of support vector machines for classification. The system was tested on 18 trauma CT datasets and achieved 95.3% sensitivity and 1.7 false positives per case by leave-one-out cross validation.

Pitfalls that may mimic injuries of the triangular fibrocartilage and proximal intrinsic wrist ligaments at MR imaging.

Diagnosis of injuries to the ligamentous structures of the wrist can be a challenge, particularly when there is involvement of the small, complex structures of the proximal wrist. Recent advances in magnetic resonance (MR) imaging, especially in spatial and contrast resolution, have facilitated more precise visualization of these structures. However, there are a number of pitfalls that may cause difficulty in diagnosis of injuries to the triangular fibrocartilage complex (TFCC), lunotriquetral ligament, and scapholunate ligament. Use of inappropriate MR imaging sequences and MR imaging artifacts may decrease the accuracy of diagnosis of injuries to the TFCC and wrist ligaments, whereas variant anatomy of the proximal wrist structures may mimic disease of the TFCC and wrist ligaments. Knowledge of the detailed anatomy of the wrist, as well as variant patterns of structure morphology and signal intensity, can help differentiate actual disease from normal or variant appearances at assessment with MR imaging.