Wavelet Guided 3D Deep Model to improve Dental Microfracture Detection.

Epidemiological studies indicate that microfractures (cracks) are the third most common cause of tooth loss in industrialized countries. An undetected crack will continue to progress, often with significant pain, until the tooth is lost. Previous attempts to utilize cone beam computed tomography (CBCT) for detecting cracks in teeth had very limited success. We propose a model that detects cracked teeth in high resolution (hr) CBCT scans by combining signal enhancement with a deep CNNbased crack detection model. We perform experiments on a dataset of 45 ex-vivo human teeth with 31 cracked and 14 controls. We demonstrate that a model that combines classical wavelet-based features with a deep 3D CNN model can improve fractured tooth detection accuracy in both micro-Computed Tomography (ground truth) and hr-CBCT scans. The CNN model is trained to predict a probability map showing the most likely fractured regions. Based on this fracture probability map we detect the presence of fracture and are able to differentiate a fractured tooth from a control tooth. We compare these results to a 2D CNN-based approach and we show that our approach provides superior detection results. We also show that the proposed solution is able to outperform oral and maxillofacial radiologists in detecting fractures from the hr-CBCT scans. Early detection of cracks will lead to the design of more appropriate treatments and longer tooth retention.

Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers.

Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists. In this article, we describe HistomicsML2, a software tool for learn-by-example training of machine learning classifiers for histologic patterns in whole-slide images. This tool improves training efficiency and classifier performance by guiding users to the most informative training examples for labeling and can be used to develop classifiers for prospective application or as a rapid annotation tool that is adaptable to different cancer types. HistomicsML2 runs as a containerized server application that provides web-based user interfaces for classifier training, validation, exporting inference results, and collaborative review, and that can be deployed on GPU servers or cloud platforms. We demonstrate the utility of this tool by using it to classify tumor-infiltrating lymphocytes in breast carcinoma and cutaneous melanoma. SIGNIFICANCE: An interactive machine learning tool for analyzing digital pathology images enables cancer researchers to apply this tool to measure histologic patterns for clinical and basic science studies.

Assessment of the Alveolar Capillary Network in the Postnatal Mouse Lung in 3D Using Serial Block-Face Scanning Electron Microscopy.

The alveolar capillary network (ACN) has a large surface area that provides the basis for an optimized gas exchange in the lung. It needs to adapt to morphological changes during early lung development and alveolarization. Structural alterations of the pulmonary vasculature can lead to pathological functional conditions such as in bronchopulmonary dysplasia and various other lung diseases. To understand the development of the ACN and its impact on the pathogenesis of lung diseases, methods are needed that enable comparative analyses of the complex three-dimensional structure of the ACN at different developmental stages and under pathological conditions. In this study a newborn mouse lung was imaged with serial block-face scanning electron microscopy (SBF-SEM) to investigate the ACN and its surrounding structures before the alveolarization process begins. Most parts but not all of the examined ACN contain two layers of capillaries, which were repeatedly connected with each other. A path from an arteriole to a venule was extracted and straightened to allow cross-sectional visualization of the data along the path within a plane. This allows a qualitative characterization of the structures that erythrocytes pass on their way through the ACN. One way to define regions of the ACN supplied by specific arterioles is presented and used for analyses. Pillars, possibly intussusceptive, were found in the vasculature but no specific pattern was observed in regard to parts of the saccular septa. This study provides 3D information with a resolution of about 150 nm on the microscopic structure of a newborn mouse lung and outlines some of the potentials and challenges of SBF-SEM for 3D analyses of the ACN.

Advanced statistical analysis to classify high dimensionality textural probability-distribution matrices.

Temporomandibular Joint (TMJ) Osteoarthritis (OA) is associated with significant pain and disability. It is really hard to diagnose TMJ OA during early stages of the disease. Subchondral bone texture has been observed to change in the TMJ early during TMJ OA progression. We believe that raw probability-distribution matrices describing image texture encode important information that might aid diagnosing TMJ OA. In this paper we present novel statistical methods for High Dimensionality Low Sample Size Data (HDLSSD) to test the discriminatory power of probability-distribution matrices in computed from TMJ OA medical scans. Our results, and comparison with previous results obtained from the summary features obtained from them indicate that probability-distribution matrices are an important piece of information provided by texture analysis methods and should not be down sampled for analysis.

SCOLIOSIS SCREENING AND MONITORING USING SELF CONTAINED ULTRASOUND AND NEURAL NETWORKS.

We aim to diagnose scoliosis using a self contained ultrasound device that does not require significant training to operate. The device knows its angle relative to vertical using an embedded inertial measurement unit, and it estimates its angle relative to a vertebrae using a neural network analysis of its ultrasound images. The composition of those angles defines the angle of a vertebrae from vertical. The maximum difference between vertebrae angles collected from a scan of a spine yields the Cobb angle measure that is used to quantify scoliosis severity.

Automatic Estimation of the Optic Nerve Sheath Diameter from Ultrasound Images.

We present an algorithm to automatically estimate the diameter of the optic nerve sheath from ocular ultrasound images. The optic nerve sheath diameter provides a proxy for measuring intracranial pressure, a life threating condition frequently associated with head trauma. Early treatment of elevated intracranial pressures greatly improves outcomes and drastically reduces the mortality rate. We demonstrate that the proposed algorithm combined with a portable ultrasound device presents a viable path for early detection of elevated intracranial pressure in remote locations and without access to trained medical imaging experts.

Ultrasound Augmentation: Rapid 3-D Scanning for Tracking and On-Body Display.

By using a laser projector and high speed camera, we can add three capabilities to an ultrasound system: tracking the probe, tracking the patient, and projecting information onto the probe and patient. We can use these capabilities to guide an untrained operator to take high quality, well framed ultrasound images for computer-augmented, point-of-care ultrasound applications.

Low-Rank Atlas Image Analyses in the Presence of Pathologies.

We present a common framework, for registering images to an atlas and for forming an unbiased atlas, that tolerates the presence of pathologies such as tumors and traumatic brain injury lesions. This common framework is particularly useful when a sufficient number of protocol-matched scans from healthy subjects cannot be easily acquired for atlas formation and when the pathologies in a patient cause large appearance changes. Our framework combines a low-rank-plus-sparse image decomposition technique with an iterative, diffeomorphic, group-wise image registration method. At each iteration of image registration, the decomposition technique estimates a "healthy" version of each image as its low-rank component and estimates the pathologies in each image as its sparse component. The healthy version of each image is used for the next iteration of image registration. The low-rank and sparse estimates are refined as the image registrations iteratively improve. For unbiased atlas formation, at each iteration, the average of the low-rank images from the patients is used as the atlas image for the next iteration, until convergence. Since each iteration's atlas is comprised of low-rank components, it provides a population-consistent, pathology-free appearance. Evaluations of the proposed methodology are presented using synthetic data as well as simulated and clinical tumor MRI images from the brain tumor segmentation (BRATS) challenge from MICCAI 2012.

Port geometry and its influence on vitrectomy.

PURPOSE: To determine the influence of port geometry on water and vitreous flow rates and test novel vitreous cutter tips in specific surgical situations. METHODS: Custom 20-gauge and 25-gauge vitreous cutter tips with different sized ports were evaluated through porcine vitreous and water flow rates. Five cutter tips were designed and fabricated for specific surgical functionalities. Tips were compared with a normal control tip and evaluated by water and porcine vitreous flow rates, vacuum level required to cut porcine retina, time required to cut and aspirate a porcine lens, and surgical evaluation in enucleated porcine eyes. RESULTS: Both vitreous and water flow asymptotically approached a maximum flow as the port diameter increased. Some tips removed water faster than the normal control tip, but none removed vitreous or lens faster. Several tips required higher vacuum levels to cut retina than the normal tip. CONCLUSIONS: Increasing the port diameter of a vitreous cutter to increase flow becomes less effective as the port becomes larger. Furthermore, modifying the port geometry of a vitreous cutter affects its surgical interactions with tissue. In the future, combinational instruments or surgery specific instruments may allow the surgeon to use an optimal port for a specific surgical task.

Mixed-methods resistance training increases power and strength of young and older men.

PURPOSE: This study examined the effects of mixed-methods resistance training on young and older men to determine whether similar increases in muscle power were elicited. METHODS: Effects of 10 wk of a periodized resistance-training program designed to increase muscle size, strength, and maximal power on isometric squat strength, time course of force development, muscle fiber characteristics, and muscle activation (iEMG), as well as force and power output during squat jumps, were compared in young (YM, 30 +/- 5 yr, N = 8) and older men (OM, 61 +/- 4 yr, N = 10). RESULTS: Isometric squat strength was higher in the YM compared with OM at all testing occasions and increased over the training period by 23 +/- 15% and 40 +/- 42% for the YM and OM, respectively. The early phase of the force-time curve was shifted upward in both groups over the course of the training. During the squat jumps, the YM produced higher force and power at all test occasions and at all loads tested compared with the OM. The YM increased power output by 15 +/- 14%, 33 +/- 16%, and 26 +/- 12%, and the OM by 7 +/- 5%, 36 +/- 23%, and 25 +/- 16% for the 17 kg, and 30% and 60% 1RM loads, respectively. CONCLUSION: Although the results of this study confirm age-related reductions in muscle strength and power, the older men did demonstrate similar capacity to young men for increases in these variables via an appropriate periodized resistance-training program that includes rapid, high-power exercises.