Hans de Visser

DEVELOPING A NEXT GENERATION COLONOSCOPY SIMULATOR

Colonoscopy is considered the gold standard for detection and removal of precancerous polyps in the colon. Being a difficult procedure to master, exposure to a large variety of patient and pathology scenarios is crucial for gastroenterologists’ training. Currently, most training is done on patients under supervision of experienced gastroenterologists. Being able to undertake a majority of training on simulators would greatly reduce patient risk and discomfort. A next generation colonoscopy simulator is currently under development, which aims to address the shortfalls of existing simulators. The simulator consists of a computer simulation of the colonoscope camera view and a haptic device that allows insertion of an instrumented colonoscope to drive the simulation and provide force feedback to the user. The simulation combines physically accurate models of the colonoscope, colon and surrounding tissues and organs with photorealistic visualization. It also includes the capability to generate randomized case scenarios where complexity of the colon physiology, pathology and environmental factors, such as colon preparation, can be tailored to suit training requirements. The long term goal is to provide a metrics based training and skill evaluation system that is not only useful for trainee instruction but can be leveraged for skills maintenance and eventual certification.

Uninformative Frame Detection in Colonoscopy Through Motion, Edge and Color Features

Colonoscopy is performed by using a long endoscope inserted in the colon of patients to inspect the internal mucosa. During the intervention, clinicians observe the colon under bright light to diagnose pathology and guide intervention. We are developing a computer aided system to facilitate navigation and diagnosis. One essential step is to estimate the camera pose relative to the colon from video frames. However, within every colonoscopy video is a large number of frames that provide no structural information e.g. blurry or out of focus frames or those close to the colon wall. This hampers our camera pose estimation algorithm. To distinguish uninformative frames from informative ones, we investigated several features computed from each frame: corner and edge features matched with the previous frame, the percentage of edge pixels, and the mean and standard deviation of intensity in hue-saturation-value color space. A Random Forest classifier was used for classification. The method was validated on four colonoscopy videos that were manually classified. The resulting classification had a sensitivity of 75i¾ź% and specificity of 97i¾ź% for detecting uninformative frames. The proposed features not only compared favorably to existing techniques for detecting uninformative frames, but they also can be utilized for the camera navigation purpose.

Are coupled rotations in the lumbar spine largely due to the osseo-ligamentous anatomy? - A modeling study

Prior studies have found that primary rotations in the lumbar spine are accompanied by coupled out-of-plane rotations. However, it is not clear whether these accompanying rotations are primarily due to passive (discs, ligaments and facet joints) or active (muscles) spinal anatomy. The aim of this study was to use a finite element (FE) model of the lumbar spine to predict three-dimensional coupled rotations between the lumbar vertebrae, due to passive spinal structures alone. The FE model was subjected to physiologically observed whole lumbar spine rotations about in vivo centres of rotation. Model predictions were validated by comparison of intra-discal pressures and primary rotations with in vivo measurements and these showed close agreement. Predicted coupled rotations matched in vivo measurements for all primary motions except lateral bending. We suggest that coupled rotations accompanying primary motions in the sagittal (flexion/extension) and transverse (axial rotation) planes are primarily due to passive spinal structures. For lateral bending the muscles most likely play a key role in the coupled rotation of the spine.

Deforming a High-Resolution Mesh in Real-Time by Mapping onto a Low-Resolution Physical Model

For interactive surgical simulation the physical model of the soft tissue needs to be solved in real-time. This limits the attainable model density to well below the desired mesh density for visual realism. Previous work avoids this problem by using a high-resolution visual mesh mapped onto a low-resolution physical model. We apply the same approach and present an computationally cheap implementation of a known algorithm to avoid texture artefacts caused by the mapping. We also introduce a spline-based algorithm to prevent groups of high-resolution vertices, mapped to the same low-resolution triangle, from exhibiting movements in which the underlying low-resolution structure can be recognised. The resulting mapping algorithm is very efficient, mapping 54,000 vertices in 8.5 ms on the CPU and in 0.88 ms on the GPU. Consequently, the density of the high-resolution visual mesh is limited only by the detail of the CT data from which the mesh was generated.