SPARCNN: SPAtially Related Convolutional Neural Networks

The ability to accurately detect and classify objects at varying pixel sizes in cluttered scenes is crucial to many Navy applications. However, detection performance of existing state-of the-art approaches such as convolutional neural networks (CNNs) degrade and suffer when applied to such cluttered and multi-object detection tasks. We conjecture that spatial relationships between objects in an image could be exploited to significantly improve detection accuracy, an approach that had not yet been considered by any existing techniques (to the best of our knowledge) at the time the research was conducted. We introduce a detection and classification technique called Spatially Related Detection with Convolutional Neural Networks (SPARCNN) that learns and exploits a probabilistic representation of inter-object spatial configurations within images from training sets for more effective region proposals to use with state-of-the-art CNNs. Our empirical evaluation of SPARCNN on the VOC 2007 dataset shows that it increases classification accuracy by 8% when compared to a region proposal technique that does not exploit spatial relations. More importantly, we obtained a higher performance boost of 18.8% when task difficulty in the test set is increased by including highly obscured objects and increased image clutter.Comment: 6 pages, AIPR 2016 submissio

Body image distortions following spinal cord injury

Background: Following spinal cord injury (SCI) or anaesthesia, people may continue to experience feelings of the size, shape, and posture of their body, suggesting that the conscious body image is not fully determined by immediate sensory signals. How this body image is affected by changes in sensory inputs from, and motor outputs to the body remains unclear. Methods: We tested paraplegic and tetraplegic SCI patients on a task that yields quantitative measures of body image. Participants were presented with an anchoring stimulus on a computer screen and told to imagine that the displayed body part was part of a standing mirror image of themselves. They then identified the position on the screen, relative to the anchor, where each of several parts of their body would be located. Veridical body dimensions were identified based on measurements and photographs of participants. Results: Compared to age-matched controls, paraplegic and tetraplegic patients alike perceived their torso and limbs as elongated relative to their body width. No effects of lesion level were found. Conclusions: The common distortions in body image across patient groups, despite differing SCI levels, imply that a body image may be maintained despite chronic sensory and motor loss. Systematic alterations in body image follow SCI, though our results suggest these may reflect prolonged changes in body posture and wheelchair use, rather than loss of specific sensorimotor pathways. These findings provide new insight into how the body image is maintained, and may prove useful in treatments that intervene to manipulate the body image

Computer-Aided Detection of Pathologically Enlarged Lymph Nodes On Non-Contrast CT In Cervical Cancer Patients For Low-Resource Settings

The mortality rate of cervical cancer is approximately 266,000 people each year, and 70% of the burden occurs in Low- and Middle- Income Countries (LMICs). Radiation therapy is the primary modality for treatment of locally advanced cervical cancer cases. In the absence of high quality diagnostic imaging needed to identify nodal metastasis, many LMIC sites treat standard pelvic fields, failing to include node metastasis outside of the field and/or to boost lymph nodes in the abdomen and pelvis. The first goal of this project was to create a program which automatically identifies positive cervical cancer lymph nodes on non-contrast daily CT images, which are widely available in LMICs(1). A region of interest which is likely to contain the nodal volumes relevant for cervical cancer was defined on a single patient CT(2). This region was deformed onto new patients using an in-house, demons-based deformation software. Edge detection and erosion filtering were used to distinguish potential positive nodes from normal structures. Regions on adjacent slices were then connected into a potential nodal 3D-structure. To differentiate these 3D structures from normal tissues, eighty-six features were generated based on the shape and mean pixel values of the structures, and four classification ensemble methods were tested to differentiate the positive nodes from normal tissues. A cohort of fifty-eight MD Anderson cervical cancer patients with pathologically enlarged lymph nodes were used as a training-test set. Similarly, twenty MD Anderson cervical cancer patients were obtained as a validation set. They contained 154 and 35 pathologically enlarged lymph nodes, respectively. Model comparison led to the selection of the Adaboost ensemble model, utilizing 17 features. In the validation set, 60% of the clinically significant positive cervical cancer nodes were identified along with a false/true positive ratio of ~4:1. The entire process takes approximately 10/number-of-cores-minutes. Our findings demonstrated that our computer-aided detection model can assist in the identification of metastatic nodal disease where high quality diagnostic imaging is not readily available. By identifying these nodes, radiation treatment fields can be modified to include pathologically enlarged lymph nodes, which is an essential element to providing potentially curative radiotherapy for cervical cancer

Heat Transfer and Pressure Drop in a Developing Channel Flow with Streamwise Vortices

Experiments to assess the heat transfer and pressure-drop effects of delta-wing vortex generators placed at the entrance of developing channel flows are reported in this study. The experimental geometry simulates common heat exchanger configurations and tests are conducted over a velocity range important to heating, air conditioning and refrigeration. An innovative liquid-crystal thermography technique is used to determine the local and average Nusselt numbers for an isoflux channel wall, and conventional methods are used to determine the Fanning friction factor. Vortex generators with aspect ratios of A = 2 and A = 4 are studied at attack angles of a. = 20?? to 45????. The results indicate that the streamwise vortices generated by a delta wing can enhance local Nusselt numbers by more than 200% in a developing channel flow. Under some conditions, the spatially average Nusselt number nearly doubled for a heat transfer area that was 37 to 63 times the wing area. The Fanning friction factor increased by a few percent to nearly 60%, depending on the Reynolds number.Air Conditioning and Refrigeration Project 4