Complete abdomen and pelvis segmentation using U-net variant architecture.

Purpose: Organ segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs-at-risk, which is laborious and time-consuming. We present a fully automated segmentation method based on the three-dimensional (3D) U-Net convolutional neural network (CNN) capable of whole abdomen and pelvis segmentation into 33 unique organ and tissue structures, including tissues that may be overlooked by other automated segmentation approaches such as adipose tissue, skeletal muscle, and connective tissue and vessels.

Assessing Nasal Soft-Tissue Envelope Thickness for Rhinoplasty: Normative Data and a Predictive Algorithm.

Importance: Preoperative assessment of nasal soft-tissue envelope (STE) thickness is an important component of rhinoplasty that presently lacks validated tools.

Objective: To measure and assess the distribution of nasal STE thickness in a large patient population and to determine if facial plastic surgery clinicians can predict nasal STE thickness based on visual examination of the nose.

Design, Setting, And Participants: This retrospective review and prospective assessment of 190 adult patients by 4 expert raters was conducted at an academic tertiary referral center.

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.

Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to "learn" from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems.

Development of data integration and visualization tools for the Department of Radiology to display operational and strategic metrics.

Visualizing process metrics can help identify targets for improvement initiatives. Dashboards and scorecards are tools to visualize important metrics in an easily interpretable manner. We describe the development of two visualization systems: a dashboard to provide real-time situational awareness to frontline coordinators, and a scorecard to display aggregate monthly performance metrics for strategic process improvement efforts.

Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning.

Purpose To develop and evaluate a fully automated algorithm for segmenting the abdomen from CT to quantify body composition. Materials and Methods For this retrospective study, a convolutional neural network based on the U-Net architecture was trained to perform abdominal segmentation on a data set of 2430 two-dimensional CT examinations and was tested on 270 CT examinations. It was further tested on a separate data set of 2369 patients with hepatocellular carcinoma (HCC).

Development and utilization of a web-based application as a robust radiology teaching tool (radstax) for medical student anatomy teaching.

Rationale and Objectives: The primary role of radiology in the preclinical setting is the use of imaging to improve students' understanding of anatomy. Many currently available Web-based anatomy programs include either suboptimal or overwhelming levels of detail for medical students.Our objective was to develop a user-friendly software program that anatomy instructors can completely tailor to match the desired level of detail for their curriculum, meets the unique needs of the first- and the second-year medical students, and is compatible with most Internet browsers and tablets.