From resilience thinking to Resilience Planning: Lessons from practice.

Resilience thinking has frequently been proposed as an alternative to conventional natural resource management, but there are few studies of its applications in real-world settings. To address this gap, we synthesized experiences from practitioners that have applied a resilience thinking approach to strategic planning, called Resilience Planning, in regional natural resource management organizations in Australia. This case represents one of the most extensive and long-term applications of resilience thinking in the world today. We conducted semi-structured interviews with Resilience Planning practitioners from nine organizations and reviewed strategic planning documents to investigate: 1) the key contributions of the approach to their existing strategic planning, and 2) what enabled and hindered the practitioners in applying and embedding the new approach in their organizations. Our results reveal that Resilience Planning contributed to developing a social-ecological systems perspective, more adaptive and collaborative approaches to planning, and that it clarified management goals of desirable resource conditions. Applying Resilience Planning required translating resilience thinking to practice in each unique circumstance, while simultaneously creating support among staff, and engaging external actors. Embedding Resilience Planning within organizations implied starting and maintaining longer-term change processes that required sustained multi-level organizational support. We conclude by identifying four lessons for successfully applying and embedding resilience practice in an organization: 1) to connect internal "entrepreneurs" to "interpreters" and "networkers" who work across organizations, 2) to assess the opportunity context for resilience practice, 3) to ensure that resilience practice is a learning process that engages internal and external actors, and 4) to develop reflective strategies for managing complexity and uncertainty.

"True" color surface anatomy: mapping the Visible Human to patient-specific CT data.

The mapping of "true" color and texture information into traditional medical imaging modality data can add considerable information and aid in diagnostics. One of the goals of this work has been to create CT color lookup tables for all visually well-defined structures in the Visible Human male cryosection data set which then can be used to color patient-specific CT data. The primary goal has been to develop a method for stripping textures from a volumetric data set for polygonal models and non-uniform rational B-spline (NURBS) models generated from a volumetric data set. It is believed that these methods can eventually be used to provide clinicians with 3D models with physiologically accurate color textures.

Patient specific color texture mapping of CT-based anatomical surface models utilizing cryosection data.

In traditional medical imaging modalities, color and texture information can add considerable information for diagnostics. Presently, multimodal images of a patient are unregistered and referenced independent of each other, or registered and fused into a single hybrid volume. Doctors and other medical professionals need to be able to visualize and interrogate, on a per-patient basis, a wide variety of 2D and 3D data representations that can be created from non-invasive imaging modalities, such as MRI and CT. In addition, any colorization that may be applied to the image data is strictly based on tissue density, radiation emission, or magnetic signature, and not on any physiological foundation. In order for "true-to-life" color information to be incorporated with non-invasive imaging techniques, and for it to be of consistent quality across the entire body, a single whole-body cryosection specimen with associated medical image data is needed. The National Library of Medicine's Visible Human Project offers just such a specimen. Using the full-body medical image data along with the cryosection images of the Visible Human subject, a set of color lookup tables for all visually well defined structures and organs can be created. As a result, patient-specific colorization based on real tissue color and characteristics can be incorporated into traditional intensity-based imaging modalities. The primary goal of this work has been to create CT color lookup tables for all visually well-defined structures in the Visible Human male cryosection data set. The subsequent goal has been to develop a method for stripping textures from a volumetric data set for polygonal models and non-uniform rational B-spline (NURBS) models, also generated from the volumetric data.

Volume rendering of visible human data for an anatomical virtual environment.

In this work, we utilize the axial anatomical human male sections from the National Library of Medicine's Visible Human Project to generate three-dimensional (3-D) volume representations of the human male subject. The two-dimensional (2-D) projection images were produced by combining ray tracing techniques with automated image segmentation routines. The resultant images provide accurate and realistic volumetric representations of the Visible Human data set which is ultimately needed in medical virtual environment simulation. Ray tracing techniques provide methods by which 2-D volume views of a 3-D voxel array can be produced. The cross-sectional images can be scanned at different angles to produce rotated views of the voxel array. By combining volume views at incremental angles over 360 degrees a full volumetric representation of the voxel array, in this case the human male data set, can be computer generated and displayed without the speed and memory limitations of trying to display the entire data array. Additional texture and feature information can be obtained from the data by applying optical property equations to the ray scans. The imaging effects that can be added to volume renderings using these equations include shading, shadowing, and transparency. The automated segmentation routines provide a means to distinguish between various anatomical structures of the body. These routines can be used to differentiate between skin, fat, muscle, cartilage, blood vessels, and bone. By combining automated segmentation routines with the ray-tracing techniques, 2-D volume views of various anatomical structures and features can be isolated from the full data set. Examples of these segmentation abilities are demonstrated for the human male data set which include volume views of the skeletal systems, the musculoskeletal system, and part of the vascular system. The methods described above allow us to generate lifelike images, NURBS surface models, and realistic texture maps of specific anatomical structures. We have the capability to generate images that are both accurate and lifelike, much like photographic anatomical atlases. We can also generate images, models, and textures that have the clarity of medical artwork/illustrations, by highlighting the coloring of the ray traced structures with conventional colors instead of the natural color of the specimen. We are currently in the process of generating a comprehensive reference atlas of volume rendered images of the human body, soon to be published by Mosby-Year Book. The segmentation techniques needed to create this atlas also offer the accuracy and realism needed to create surface models and texture maps for a virtual environment for surgery simulation.