A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation.

Background: We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning.

Methods: The algorithm identifies the region of interest (ROI) automatically by detecting anatomical landmarks around the specific organs using a deep reinforcement learning technique. The segmentation is restricted to this ROI and performed by a deep image-to-image network (DI2IN) based on a convolutional encoder-decoder architecture combined with multi-level feature concatenation.

RNNSLAM: Reconstructing the 3D colon to visualize missing regions during a colonoscopy.

Colonoscopy is the gold standard for pre-cancerous polyps screening and treatment. The polyp detection rate is highly tied to the percentage of surveyed colonic surface. However, current colonoscopy technique cannot guarantee that all the colonic surface is well examined because of incomplete camera orientations and of occlusions.

Club Fit: Development of a Physical Activity and Healthy Eating Intervention at a Boys & Girls Club After School Program.

Children and adolescents from minority and low income backgrounds face social and environmental challenges to engaging in physical activity and healthy eating to maintain a healthy weight. In this study, we present pilot work to develop and implement a multi-component physical activity and healthy eating intervention at a Boys & Girls Club (BGC) afterschool program. Using a community-based participatory approach, BGC staff and academic researchers developed intervention components informed by formative studies and based on a Social Ecological Theory framework.