Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work  present a method for dense reconstruction of anatomical structures using white light endoscopic imagery based on a learning process that estimates a mapping beten light reflectance and surface geometry. Our method is unique in that few unrealistic assumptions are considered (i.e.,  do not assume a Lambertian reflectance model nor do  assume a point light source) and  learn a model on a per-patient basis, thus increasing the accuracy and extensibility to different endoscopic sequences. The proposed method assumes accurate video-CT registration through a combination of Structure-from-Motion (SfM) and Trimmed-ICP, and then uses the registered 3D structure and motion to generate training data with which to learn a multivariate regression of observed pixel values to known 3D surface geometry.  demonstrate with a non-linear regression technique using a neural network towards estimating depth images and surface normal maps, resulting in high-resolution spatial 3D reconstructions to an average error of 0.53mm (on the low side, when anatomy matches the CT precisely) to 1.12mm (on the high side, when the presence of liquids causes scene geometry that is not present in the CT for evaluation). Our results are exhibited on patient data and validated with associated CT scans. In total,  processed 206 total endoscopic images from patient data, where each image yields approximately 1 million reconstructed 3D points per image. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
['Austin Reiter', 'Simon Leonard', 'Ayushi Sinha', 'Masaru Ishii', 'Russell H. Taylor', 'Gregory D. Hager']
I am writing a research paper on a idea Endoscopic-CT: learning-based photometric reconstruction for endoscopic sinus surgery
[{"from": "human", "value": "I am writing a research paper on a idea Endoscopic-CT: learning-based photometric reconstruction for endoscopic sinus surgery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work  present a method for dense reconstruction of anatomical structures using white light endoscopic imagery based on a learning process that estimates a mapping beten light reflectance and surface geometry. Our method is unique in that few unrealistic assumptions are considered (i.e.,  do not assume a Lambertian reflectance model nor do  assume a point light source) and  learn a model on a per-patient basis, thus increasing the accuracy and extensibility to different endoscopic sequences. The proposed method assumes accurate video-CT registration through a combination of Structure-from-Motion (SfM) and Trimmed-ICP, and then uses the registered 3D structure and motion to generate training data with which to learn a multivariate regression of observed pixel values to known 3D surface geometry.  demonstrate with a non-linear regression technique using a neural network towards estimating depth images and surface normal maps, resulting in high-resolution spatial 3D reconstructions to an average error of 0.53mm (on the low side, when anatomy matches the CT precisely) to 1.12mm (on the high side, when the presence of liquids causes scene geometry that is not present in the CT for evaluation). Our results are exhibited on patient data and validated with associated CT scans. In total,  processed 206 total endoscopic images from patient data, where each image yields approximately 1 million reconstructed 3D points per image. \u00a9 (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only."}]