3D view reconstruction from endoscopic videos for gastrointestinal tract surgery planning

Conference paper

Gao, X., Rahmanti, A. and Braden, B. 2025. 3D view reconstruction from endoscopic videos for gastrointestinal tract surgery planning. Kim, J., Conceição, R., Yousef, M., Bhavsar, A., Pelayo, S., Fred, A. and Gamboa, H. (ed.) 18th International Joint Conference on Biomedical Engineering Systems and Technologies. Porto, Portugal 20 - 22 Feb 2025 SCITEPRESS - Science and Technology Publications. pp. 221-228 https://doi.org/10.5220/0013125000003911
TypeConference paper
Title3D view reconstruction from endoscopic videos for gastrointestinal tract surgery planning
AuthorsGao, X., Rahmanti, A. and Braden, B.
Abstract

This paper investigates the application of neural radiance field (NeRF) to reconstruct a 3D model from 2D endoscopic videos for surgical planning and removal of gastrointestinal lesions. It comprises three stages. The first one is video preprocess to remove frames with artefact of colour misalignment based on a deep learning network. Then the remaining frames are converted into NeRF compatible format. This stage includes extraction of camera information regarding intrinsic, extrinsic and ray pathway parameters as well as conversion to NeRF format based on COLMAP library, a pipeline built upon structure-from-motion (SfM) with multi-view stereo (MVS). Finally the training takes place for establishment of NeRF model implemented upon Nerfstudio library. Initial results illustrate that this end-to-end, i.e. from 2D video input to 3D model output deep learning architecture presents great potentials for reconstruction of gastrointestinal tract. Base on the two sets of data containing 2600 i mages, the similarity measures of SSIM, PSNR and LPIPS between original (ground truth) and rendered images are 19.46 ± 2.56, 0.70 ± 0.054, and 0.49 ± 0.05 respectively. Future work includes enlarging dataset and removal of ghostly artefact from rendered images.

KeywordsDeep Learning; 3D Reconstruction; Nerfs; Gastrointestinal Tract; SfM; 2D Endoscopic Video; Endoscopic Artefacts
Sustainable Development Goals3 Good health and well-being
Middlesex University ThemeHealth & Wellbeing
Conference18th International Joint Conference on Biomedical Engineering Systems and Technologies
Page range221-228
Proceedings TitleProceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
EditorsKim, J., Conceição, R., Yousef, M., Bhavsar, A., Pelayo, S., Fred, A. and Gamboa, H.
ISSN2184-4305
ISBN9789897587313
PublisherSCITEPRESS - Science and Technology Publications
Publication dates
Print20 Feb 2025
Publication process dates
Accepted04 Dec 2024
Deposited14 Mar 2025
Output statusPublished
Publisher's version
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File Access Level
Open
Copyright Statement

Paper published under CC license (CC BY-NC-ND 4.0)

Digital Object Identifier (DOI)https://doi.org/10.5220/0013125000003911
Web address (URL) of conference proceedingshttp://doi.org/10.5220/0000197500003911
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Gao, X., Batty, S., Clark, J., Fryer, T. and Turkheimer, F. 2002. Towards archiving 3D PET brain images based on their physiological and visual content. International conference on diagnostic imaging and analysis. Shanghai, China 18 - 20 Aug 2002
A new approach to estimation of non-isotropic scale factors for correction of MR distortion
Gao, X., Hui, R., White, A.S. and Tian, Z. 2009. A new approach to estimation of non-isotropic scale factors for correction of MR distortion. International Journal of Computer Assisted Radiology and Surgery. 4 (s1), pp. s349-s350.
Classification of images on the basis of the properties of informative regions.
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2003. Classification of images on the basis of the properties of informative regions. Pattern Recognition and Image Analysis. 13 (2), pp. 349-352.
A fast approach to segmentation of PET brain images for extraction of features
Gao, X. and Clark, J. 2008. A fast approach to segmentation of PET brain images for extraction of features. Gao, X., Loomes, M., Comley, R., Muller, H. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_25
Prototype system for semantic retrieval of neurological PET images
Batty, S., Clark, J., Fryer, T. and Gao, X. 2008. Prototype system for semantic retrieval of neurological PET images. Gao, X., Muller, H., Loomes, M., Comley, R. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_23
Colour vision model-based approach for segmentation of traffic signs
Gao, X., Hong, K., Passmore, P., Podladchikova, L. and Shaposhnikov, D. 2008. Colour vision model-based approach for segmentation of traffic signs. EURASIP Journal on Image and Video Processing. 2008. https://doi.org/10.1155/2008/386705
Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach
Anishenko, S., Osimov, V., Shaposhnikov, D., Podladchikova, L., Comley, R. and Gao, X. 2008. Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach. Puuronen, S., Pechenizkiy, M., Tsymbal, A. and Lee, D. (ed.) 21st IEEE International Symposium on Computer-Based Medical Systems. Jyvaskyla, Finland 17 - 19 Jun 2008 IEEE Computer Society. pp. 50-52 https://doi.org/10.1109/CBMS.2008.19
Detection of head motions using a vision model
Gao, X., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J. 2007. Detection of head motions using a vision model. Bashshur, R. (ed.) 3rd IASTED International Conference on Telehealth. Montreal, QC, Canada 31 May - 01 Jun 2007 Anaheim, CA Acta Press. pp. 167-171
Recognition of traffic signs based on their colour and shape features extracted using human vision models
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K. and Shevtsova, N. 2006. Recognition of traffic signs based on their colour and shape features extracted using human vision models. Journal of Visual Communication and Image Representation. 17 (4), pp. 675-685. https://doi.org/10.1016/j.jvcir.2005.10.003
Colour management in telemedicine
Gao, X. 2004. Colour management in telemedicine. Hamza, M. (ed.) 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 Anaheim, CA Acta Press. pp. 361-364
Application of vision models to traffic sign recognition
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2004. Application of vision models to traffic sign recognition. Kaynak, O., Alpaydin, E., Oja, E. and Xu, L. (ed.) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Istanbul, Turkey 26 - 29 Sep 2003 Berlin, Heidelberg Springer. https://doi.org/10.1007/3-540-44989-2_131
Towards content-based retrieval for wallpaper images
Qian, Y., Tully, C., Hendon, Z. and Gao, X. 2003. Towards content-based retrieval for wallpaper images. 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 pp. 305-309
Vision models based identification of traffic signs
Gao, X., Podladchikova, L., Shaposhnikov, D., Shevtsova, N., Hong, K., Batty, S., Golovan, A. and Gusakova, V. 2002. Vision models based identification of traffic signs. 1st European Conference on Colour Graphics, Imaging, and Vision. University of Poitiers, France 02 - 05 Apr 2002 Society for Imaging Science and Technology.
Content based retrieval of lesioned brain images
Batty, S., Blandford, A., Clark, J., Fryer, T. and Gao, X. 2002. Content based retrieval of lesioned brain images. Siegel, E. and Huang, H. (ed.) SPIE Medical Imaging 2002. San Diego, California, United States 23 - 28 Feb 2002 Bellingham Society of Photo-optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.466997
A method of vessel tracking for vessel diameter measurement on retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2001. A method of vessel tracking for vessel diameter measurement on retinal images. 2001 International Conference on Image Processing. Thessaloniki, Greece 07 - 10 Oct 2001 IEEE.
Computer algorithms for the automated measurement of retinal arteriolar diameters
Chapman, N., Witt, N., Gao, X., Bharath, A., Stanton, A., Thom, S. and Hughes, A. 2001. Computer algorithms for the automated measurement of retinal arteriolar diameters. British Journal of Ophthalmology. 85 (1), pp. 74-79. https://doi.org/10.1136/bjo.85.1.74
Quantification and characterization of arteries in retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2000. Quantification and characterization of arteries in retinal images. Computer Methods and Programs in Biomedicine. 63 (2), pp. 133-146. https://doi.org/10.1016/S0169-2607(00)00082-1