Automatic Segmentation of Abdominal Wall in Ventral Hernia CT: A Pilot Study

Abdominal wall extraction using constrained deformable model and abdominal context

Information about abdominal wall can be used for many applications from organ segmentation, registration, and surgical simulation. The challenges exist in abdominal wall extraction due to its varieties in shapes, connection to the internal organs and anterior layer edge formed between the muscle and fascia/fatty layer, which may distract the shape model. In this paper we present an approach to the posterior abdominal wall extraction using the shape model and other abdominal context, particularly with the rib-spine bone information and the wall image features. The shape model is constructed based on the training abdominal walls that are delineated manually. After bone information being extracted, the wall shape deforms from the prior shape model using the snake, which is constrained by the bone context and guided by the processed image energy map with the aim of removing distracted image features of anterior abdominal wall and the outer region from the original map. Meanwhile, an overall convex shape is maintained by limiting the angles of the contour points. The proposed approach is tested on abdominal CT data which provides encouraging results.

Immersive Virtual Reality for Visualization of Abdominal CT

Immersive virtual environments use a stereoscopic head-mounted display and data glove to create high fidelity virtual experiences in which users can interact with three-dimensional models and perceive relationships at their true scale. This stands in stark contrast to traditional PACS-based infrastructure in which images are viewed as stacks of two-dimensional slices, or, at best, disembodied renderings. Although there has substantial innovation in immersive virtual environments for entertainment and consumer media, these technologies have not been widely applied in clinical applications. Here, we consider potential applications of immersive virtual environments for ventral hernia patients with abdominal computed tomography imaging data. Nearly a half million ventral hernias occur in the United States each year, and hernia repair is the most commonly performed general surgery operation worldwide. A significant problem in these conditions is communicating the urgency, degree of severity, and impact of a hernia (and potential repair) on patient quality of life. Hernias are defined by ruptures in the abdominal wall (i.e., the absence of healthy tissues) rather than a growth (e.g., cancer); therefore, understanding a hernia necessitates understanding the entire abdomen. Our environment allows surgeons and patients to view body scans at scale and interact with these virtual models using a data glove. This visualization and interaction allows users to perceive the relationship between physical structures and medical imaging data. The system provides close integration of PACS-based CT data with immersive virtual environments and creates opportunities to study and optimize interfaces for patient communication, operative planning, and medical education.