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PurposeFor basic training in ultrasonography (US), medical students and residents must learn cross-sectional anatomy. However, the present educational material is not sufficient to learn the sectional anatomy for US. This study aimed to provide a criterion for reading ambiguous structures on US images of upper limb through the sectioned images of Visible Korean.MethodsUS images of the right arm of a volunteer were scanned (28 planes). For comparison with US images, the sectioned images of the right upper limb (24 bits color, 0.5 mm intervals, 0.06 mm × 0.06 mm sized pixel) were used. After the volume model was constructed from the sectioned images using MRIcroGL, new sectioned images of 28 planes corresponding to the US images of 28 planes were created by adjusting the slope of the volume model. In all images, the anatomical terms of 59 structures from the shoulder to the fingers were annotated.ResultsIn the atlas, which consists of 28 sets of US images and sectioned images of various slope planes, 59 structures of the shoulder, arm, elbow, wrist, palm, and fingers were observed in detail.ConclusionWe were able to interpret the ambiguous structures on the US images using the sectioned images with high resolution and actual color. Therefore, to learn the cross-sectional anatomy for US, the sectioned images from the Visible Korean project were deemed to be the suitable data because they contained all human gross anatomical information.

Background and Objectives: In the field of orthopedic surgery, novel techniques of three-dimensional shape modeling using two-dimensional tomographic images are used for bone-shape measurements, preoperative planning in joint-replacement surgery, and postoperative evaluation. ZedView® (three-dimensional measurement instrument and preoperative-planning software) had previously been developed. Our group is also using ZedView® for preoperative planning and postoperative evaluation for more accurate implant placement and osteotomy. This study aimed to evaluate the measurement error in this software in comparison to a three-dimensional measuring instrument (3DMI) using human bones. Materials and Methods: The study was conducted using three bones from cadavers: the pelvic bone, femur, and tibia. Three markers were attached to each bone. Study 1: The bones with markers were fixed on the 3DMI. For each bone, the coordinates of the center point of the markers were measured, and the distances and angles between these three points were calculated and defined as “true values.” Study 2: The posterior surface of the femur was placed face down on the 3DMI, and the distances from the table to the center of each marker were measured and defined as “true values.” In each study, the same bone was imaged using computed tomography, measured with this software, and the measurement error from the corresponding “true values” was calculated. Results: Study 1: The mean diameter of the same marker using the 3DMI was 23.951 ± 0.055 mm. Comparisons between measurements using the 3DMI and this software revealed that the mean error in length was <0.3 mm, and the error in angle was <0.25°. Study 2: In the bones adjusted to the retrocondylar plane with the 3DMI and this software, the average error in the distance from the planes to each marker was 0.43 (0.32–0.58) mm. Conclusion: This surgical planning software could measure the distance and angle between the centers of the markers with high accuracy; therefore, this is very useful for pre- and postoperative evaluation.

Engineered mice play an ever-increasing role in defining connections between genotype and phenotypic expression. The potential of magnetic resonance microscopy (MRM) for morphologic phenotyping in the mouse has previously been demonstrated; however, applications have been limited by long scan times, availability of the technology, and a foundation of normative data. This article describes an integrated environment for high-resolution study of normal, transgenic, and mutant mouse models at embryonic and neonatal stages. Three-dimensional images are shown at an isotropic resolution of 19.5 μm (voxel volumes of 8 pL), acquired in 3 h at embryonic days 10.5-19.5 (10 stages) and postnatal days 0-32 (6 stages). A web-accessible atlas encompassing this data was developed, and for critical stages of embryonic development (prenatal days 14.5-18.5), >200 anatomical structures have been identified and labeled. Also, matching optical histology and analysis tools are provided to compare multiple specimens at multiple developmental stages. The utility of the approach is demonstrated in characterizing cardiac septal defects in conditional mutant embryos lacking the Smoothened receptor gene. Finally, a collaborative paradigm is presented that allows sharing of data across the scientific community. This work makes magnetic resonance microscopy of the mouse embryo and neonate broadly available with carefully annotated normative data and an extensive environment for collaborations.

BackgroundA sound knowledge of cross-sectional anatomy is needed to interpret radiological images. Ultrathin E12-plastinated slices serve as good learning resources to begin with, but effective utilisation of these resources are often challenging due to their fragility and lack of adequate laboratory time. To enhance interpretation of E12 slices, and also to promote independent learning, we developed a web-based self learning resource.MethodsAn interactive online sectional anatomy learning tool (SALT) to learn the cross-sectional anatomy of the spinal levels, thorax, abdomen and pelvis was developed using Courselab software. SALT was piloted on third-year medical students learning regional and clinical anatomy of the human body. At the end of the academic year, student participation within the resource was analysed, and the resource usage was compared with the users’ academic performance.ResultsEach aspect of SALT was accessed 338 times on average, by 51% of the class. The majority medical students accessed the resource after class hours. Continued research usage was observed on weekends and holidays, which peaked during exam periods. SALT usage also had a positive impact on the users’ academic performance (p < 0.05). Students also used the resource after exams and during their subsequent years of study.ConclusionSALT promoted independent learning, as well as enhanced students’ learning experience and academic performance. Having the benefit of online access, the resource was used almost 24/7, both on and off-campus. Educators should be encouraged to develop and trial their own simple inexpensive online resources tailormade to meet student needs and supplement to the existing traditional teaching techniques.

This study aimed to investigate the accuracy of estimating the volume of limb muscles (MV) using ultrasonographic muscle thickness (MT) measurements. The MT and MV of each of elbow flexors and extensors, knee extensors and ankle plantar flexors were determined from a single ultrasonographic image and multiple magnetic resonance imaging (MRI) scans, respectively, in 27 healthy men (23-40 years of age) who were allocated to validation ( n=14) and cross-validation groups ( n=13). In the validation group, simple and multiple regression equations using MT and a set of MT and limb length, respectively, as independent variables were derived to estimate the MV measured by MRI. However, only the multiple regression equations were cross-validated, and so the prediction equations with r(2) of 0.787-0.884 and the standard error of estimate of 22.1 cm(3) (7.3%) for the elbow flexors to 198.5 cm(3) (11.1%) for the knee extensors were developed using the pooled data. This approach did not induce significant systematic error in any muscle group, with no significant difference in the accuracy of estimating MV between muscle groups. In the multiple regression equations, the relative contribution of MT for predicting MV varied from 41.9% for the knee extensors to 70.4% for the elbow flexors. Thus, ultrasonographic MT measurement was a good predictor of MV when combined with limb length. For predicting MV, however, the unsuitability of a simple equation using MT only and the difference between muscle groups in the relative contribution of MT in multiple regression equations indicated a need for further research on the limb site selected and muscle analyzed for MT measurement.

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers. Using magnetic resonance images of the heart and aorta from 26,893 individuals in the UK Biobank, a phenome-wide association study associates cardiovascular imaging phenotypes with a wide range of demographic, lifestyle and clinical features.

Labeled medical imaging data is scarce and expensive to generate. To achieve generalizable deep learning models large amounts of data are needed. Standard data augmentation is a method to increase generalizability and is routinely performed. Generative adversarial networks offer a novel method for data augmentation. We evaluate the use of CycleGAN for data augmentation in CT segmentation tasks. Using a large image database we trained a CycleGAN to transform contrast CT images into non-contrast images. We then used the trained CycleGAN to augment our training using these synthetic non-contrast images. We compared the segmentation performance of a U-Net trained on the original dataset compared to a U-Net trained on the combined dataset of original data and synthetic non-contrast images. We further evaluated the U-Net segmentation performance on two separate datasets: The original contrast CT dataset on which segmentations were created and a second dataset from a different hospital containing only non-contrast CTs. We refer to these 2 separate datasets as the in-distribution and out-of-distribution datasets, respectively. We show that in several CT segmentation tasks performance is improved significantly, especially in out-of-distribution (noncontrast CT) data. For example, when training the model with standard augmentation techniques, performance of segmentation of the kidneys on out-of-distribution non-contrast images was dramatically lower than for in-distribution data (Dice score of 0.09 vs. 0.94 for out-of-distribution vs. in-distribution data, respectively, p < 0.001). When the kidney model was trained with CycleGAN augmentation techniques, the out-of-distribution (non-contrast) performance increased dramatically (from a Dice score of 0.09 to 0.66, p < 0.001). Improvements for the liver and spleen were smaller, from 0.86 to 0.89 and 0.65 to 0.69, respectively. We believe this method will be valuable to medical imaging researchers to reduce manual segmentation effort and cost in CT imaging.

X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.

Background The relationship between spinal alignment and skeletal muscle mass (SMM) has attracted attention in recent years. Sagittal alignment is known to deteriorate with age, but it is not known whether this is related to paraspinal muscles. Therefore, the purpose of this study is to elucidate the role of the multifidus (MF) and psoas major (PS) muscles in maintaining global spinal alignment in patients with lumbar spinal stenosis (LSS) and/or degenerative spondylolisthesis (DS), and to analyze whether each muscles’ cross-sectional area (CSA) correlates with whole-body SMM using bioimpedance analysis (BIA). Methods We retrospectively evaluated 140 patients who were hospitalized for surgery to treat LSS and/or DS. Spinal alignment, CSA of spinal muscles, and body composition parameters were measured from full-length standing whole-spine radiography, MRI, and BIA before surgery. The following standard measurements were obtained from radiographs: sagittal balance (C7-SVA), cervical lordosis (CL; C2–C7), lumbar lordosis (LL; L1–S1), thoracic kyphosis (TK; T5–T12), pelvic incidence (PI), pelvic tilt (PT), and sacral slope (SS). Results The average PS CSA (AveCSA) was highest at L4-L5, whereas MF AveCSA was highest at L5-S1. Paraspinal muscle CSAs were greater in males than in females. There was no statistically significant difference between the left and right CSA for either MF or PS. Correlation coefficient showed strong correlations between the PS AveCSA (L4-L5) and whole body SMM (r = 0.739). Correlation coefficient analysis also showed weak correlation between SMM and PT (r = − 0.184). Furthermore, PS AveCSA (L4-L5) correlated with the PT (r = − 0.183) and age (r = − 0.156), while PT correlated with the whole body SMM (r = − 0.184) but not with age. Conclusions Whole body SMM showed correlation with PS AvCSA (L4-L5) and with PT among the spinal parameters, which was the same result in MF AvCSA (L4-L5). These findings suggest that the posterior inclination of the pelvis may be correlated with paraspinal muscle area rather than age.

Automated tissue sectioning and two-photon imaging of fluorescently labeled and fixed mouse brains allows high-resolution tomographic imaging of the entire brain. The authors demonstrate performance using multiple GFP mouse lines, dye-based retrograde tracing and viral anterograde tracing. Here we describe an automated method, named serial two-photon (STP) tomography, that achieves high-throughput fluorescence imaging of mouse brains by integrating two-photon microscopy and tissue sectioning. STP tomography generates high-resolution datasets that are free of distortions and can be readily warped in three dimensions, for example, for comparing multiple anatomical tracings. This method opens the door to routine systematic studies of neuroanatomy in mouse models of human brain disorders.