INTRODUCTION:Intraoperative neuromonitoring (IONM) is commonly used during surgery of the spine and spinal cord for early surveillance of iatrogenic injury to the central and peripheral nervous system. However, for infants and young children under 3 years of age, the use of IONM is challenging due to incomplete central and peripheral myelination.CASE PRESENTATION:We report a case of a T4-T6 dermal sinus tract (DST) that was resected on day of life 23, with the successful use of IONM.CONCLUSION:To our knowledge, this is the youngest reported case of the use of IONM in the surgical correction of a DST in a neonatal patient. This case demonstrates the potential efficacy of IONM in neonatal spine surgery and the techniques used to adapt the technology to an immature nervous system.

Over the last decade, convolutional neural networks (CNNs) have emerged as the leading algorithms in image classification and segmentation. Recent publication of large medical imaging databases have accelerated their use in the biomedical arena. While training data for photograph classification benefits from aggressive geometric augmentation, medical diagnosis - especially in chest radiographs - depends more strongly on feature location. Diagnosis classification results may be artificially enhanced by reliance on radiographic annotations. This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms. A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation (similarity transform parameters) of the chest and segmentation of radiographic annotations. Chest x-rays were obtained from published databases. The algorithm was trained with 1000 manually labeled images with augmentation. Results were evaluated by expert clinicians, with acceptable geometry in 95.8% and annotation mask in 96.2% (n = 500), compared to 27.0% and 34.9% respectively in control images (n = 241). We hypothesize that this pre-processing step will improve robustness in future diagnostic algorithms.Clinical relevance-This work demonstrates a universal pre-processing step for chest radiographs - both normalizing geometry and masking radiographic annotations - for use prior to further analysis.

The medical community has recognized the importance of leadership skills among its members. While numerous leadership assessment tools exist at present, few are specifically tailored to the unique health care environment. The study team designed a 24-item survey (Healthcare Evaluation & Assessment of Leadership [HEAL]) to measure leadership competency based on the core competencies and core principles of the Duke Healthcare Leadership Model. A novel digital platform was created for use on handheld devices to facilitate its distribution and completion. This pilot phase involved 126 health care professionals self-assessing their leadership abilities. The study aimed to determine both the content validity of the survey and the feasibility of its implementation and use. The digital platform for survey implementation was easy to complete, and there were no technical problems with survey use or data collection. With regard to reliability, initial survey results revealed that each core leadership tenet met or exceeded the reliability cutoff of 0.7. In self-assessment of leadership, women scored themselves higher than men in questions related to patient centeredness (P= 0.016). When stratified by age, younger providers rated themselves lower with regard to emotional intelligence and integrity. There were no differences in self-assessment when stratified by medical specialty. While only a pilot study, initial data suggest that HEAL is a reliable and easy-to-administer survey for health care leadership assessment. Differences in responses by sex and age with respect to patient centeredness, integrity, and emotional intelligence raise questions about how providers view themselves amid complex medical teams. As the survey is refined and further administered, HEAL will be used not only as a self-assessment tool but also in "360" evaluation formats.

BACKGROUND:Previous studies have shown that contrast-enhanced multidetector computed tomography (CE-MDCT) could identify ventricular fibrosis after myocardial infarction. However, whether CE-MDCT can characterize atrial low-voltage regions remains unknown.OBJECTIVE:The purpose of this study was to examine the association of CE-MDCT image attenuation with left atrial (LA) low bipolar voltage regions in patients undergoing repeat ablation for atrial fibrillation recurrence.METHODS:We enrolled 20 patients undergoing repeat ablation for atrial fibrillation recurrence. All patients underwent preprocedural 3-dimensional CE-MDCT of the LA, followed by voltage mapping (>100 points) of the LA during the ablation procedure. Epicardial and endocardial contours were manually drawn around LA myocardium on multiplanar CE-MDCT axial images. Segmented 3-dimensional images of the LA myocardium were reconstructed. Electroanatomic map points were retrospectively registered to the corresponding CE-MDCT images.RESULTS:A total of 2028 electroanatomic map points obtained in sinus rhythm from the LA endocardium were registered to the segmented LA wall CE-MDCT images. In a linear mixed model, each unit increase in the local image attenuation ratio was associated with 25.2% increase in log bipolar voltage (P = .046) after adjusting for age, sex, body mass index, and LA volume, as well as clustering of data by patient and LA regions.CONCLUSION:We demonstrate that the image attenuation ratio derived from CE-MDCT is associated with LA bipolar voltage. The potential ability to image fibrosis via CE-MDCT may provide a useful alternative in patients with contraindications to magnetic resonance imaging.

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.

Background: Prior studies have shown that contrast enhanced multislice computed tomography (ceMSCT) can visualize ventricular scar. The purpose of this manuscript is to assess the ability of ceMDCT to detect left atrial (LA) fibrosis in patients undergoing repeat ablation for atrial fibrillation (AF). Methods and Results: We enrolled 10 patients prior to repeat ablation. All patients underwent pre-procedural ceMSCT of the LA followed by voltage mapping of the LA during the ablation procedure. The ceMSCT images were processed off-line using Seg3D software (University of Utah, Salt Lake City, USA). Epicardial and endocardial contours were manually drawn around LA myocardium on multi-planar axial images. Electroanatomic mapping points were retrospectively registered to the corresponding ceMSCT images. LA Scar identified by an image intensity range between 0-30 Hounsfeld units (HU) on ceMSCT correlated qualitatively with scar identified by low-voltage (bipolar voltage <0.1 mV). Qualitative agreement in the posterior wall and the left pulmonary vein antrum was excellent. In a linear regression model of a subset of 4 patients, the local CT image intensity was associated with local bipolar voltage (+0.1 mV +/- 0.06 / 100 HU,P=0.019) Conclutions: We demonstrate that ceMSCT can provide valuable information about LA myocardial fibrosis with excellent electro-anatomic concordance. Electroanatomic voltage map of a redo PVI patient showing LA fibrosis in red corresponding to Low voltage (<0.1 mV). Figure 1-B. Contrast enhanced multislice computed tomography imaging derived scar map of the same patient showing the same projection of the left atrium. Image intensity of LA between 0 to 30 is shown in red and blood pool of left atrium is shown in light blue.

Segmentation of the left ventricular myocardium from 3D echocardiograms is complicated by speckle, artifact, and complex anatomy. Typical segmentation methods benefit from accurate initialization. We propose a two-step Hough transform to find an annular approximation of the myocardium in short-axis echo slices. The method was compared to manual segmentation of 5641 slices by center points and endocardial and epicardial radii. Centers deviated by a mean 3.31 mm, and radii by 2.57 mm and 3.04 mm, respectively. Dice's coefficient of similarity was 0.70. Similarity is higher when apical slices are ignored. 93.8% of all slices and 96.8% of non-apical slices met criteria that suggest they would be appropriate to initialize further segmentation.

Successful segmentation of the left ventricle in echocardiograms relies on strong edge responses to ensure that segmentation methods converge to the endocardial boundary. However, segmentation methods that do not interpret edge responses using local shape information or global context are often led astray by imaging artifacts. An extension to a boundary fragment model borrowed from computer vision literature is presented as a method for determining which edge responses contribute to the endocardial boundary. To demonstrate its applicability, the proposed method is applied to a data set composed of long-axis echocardiogram slices from five subjects. Results show that the process is effective at locating the endocardium and identifying the edge responses which correspond to the endocardial boundary.

Fast segmentation of the left ventricular (LV) myocardium in 3D+time echocardiographic sequences can provide quantitative data of heart function that can aid in clinical diagnosis and disease assessment. We present an algorithm for automatic segmentation of the LV myocardium in 2D and 3D sequences which employs learning optical flow (OF) strategies. OF motion estimation is used to propagate single-frame segmentation results of the Random Forest classifier from one frame to the next. The best strategy for propagating between frames is learned on a per-frame basis. We demonstrate that our algorithm is fast and accurate. We also show that OF propagation increases the performance of the method with respect to the static baseline procedure, and that learning the best OF propagation strategy performs better than single-strategy OF propagation.

Chemical patterns prepared by self-assembly, combined with soft lithography or photolithography, are directly compared. Pattern fidelity can be controlled in both cases but patterning at the low densities necessary for small-molecule probe capture of large biomolecule targets is better accomplished using microcontact insertion printing (μCIP). Surfaces patterned by μCIP are used to capture biomolecule binding partners for the small molecules dopamine and biotin.