Jia Gu

Toward Automated Model Building from Video in Computer-Assisted Diagnoses in Colonoscopy

A 3D colon model is an essential component of a computer-aided diagnosis (CAD) system in colonoscopy to assist surgeons in visualization, and surgical planning and training. This research is thus aimed at developing the ability to construct a 3D colon model from endoscopic videos (or images). This paper summarizes our ongoing research in automated model building in colonoscopy. We have developed the mathematical formulations and algorithms for modeling static, localized 3D anatomic structures within a colon that can be rendered from multiple novel view points for close scrutiny and precise dimensioning. This ability is useful for the scenario when a surgeon notices some abnormal tissue growth and wants a close inspection and precise dimensioning. Our modeling system uses only video images and follows a well-established computer-vision paradigm for image-based modeling. We extract prominent features from images and establish their correspondences across multiple images by continuous tracking and discrete matching. We then use these feature correspondences to infer the cameras movement. The camera motion parameters allow us to rectify images into a standard stereo configuration and calculate pixel movements (disparity) in these images. The inferred disparity is then used to recover 3D surface depth. The inferred 3D depth, together with texture information recorded in images, allow us to construct a 3D model with both structure and appearance information that can be rendered from multiple novel view points.

Computer-aided diagnosis (CAD) for colonoscopy

Colorectal cancer is the second leading cause of cancer deaths, and ranks third for new cancer cases and cancer mortality for both men and women. However, its death rate can be dramatically reduced by appropriate treatment when early detection is available. The purpose of colonoscopy is to identify and assess the severity of lesions, which may be flat or protruding. Due to the subjective nature of the examination, colonoscopic proficiency is highly variable and dependent upon the colonoscopist’s knowledge and experience. An automated image processing system providing an objective, rapid, and inexpensive analysis of video from a standard colonoscope could provide a valuable tool for screening and diagnosis. In this paper, we present the design, functionality and preliminary results of its Computer-AidedDiagnosis (CAD) system for colonoscopy – ColonoCAD. ColonoCAD is a complex multi-sensor, multidata and multi-algorithm image processing system, incorporating data management and visualization, video quality assessment and enhancement, calibration, multiple view based reconstruction, feature extraction and classification. As this is a new field in medical image processing, our hope is that this paper will provide the framework to encourage and facilitate collaboration and discussion between industry, academia, and medical practitioners.

Temporal matching in endoscopic images for remote-controlled robotic surgery

Temporal matching is applied in the frame of the formation of high-level entities in remote-controlled robotic surgery. The objective is to track tumor boundaries over time to improve the segmentation stage in each image of the sequence to facilitate the tracking and localization of the tumor. It makes use of an attributed string matching technique to find the correspondence between tumor boundaries over time. Relationships are then exploited to reconstitute the tumor boundaries and remove the inconsistencies coming from the detection errors. Input data are free form shapes of different length representing the tumor boundary, extracted at a previous stage.