3-D and 2-D Reconstruction of Bladders for the Assessment of Inter-Session Detection of Tissue Changes: A Proof of Concept

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Purpose: Abnormalities in the bladder wall require careful investigation regarding type, spatial position and invasiveness. Construction of a 3-D model of the bladder is helpful to ensure adequate coverage of the scanning procedure, quantitative comparison of bladder wall textures between successive sessions and finding back previously-discovered abnormalities.

Methods: Videos of both an in-vivo bladder and a textured bladder phantom were acquired. Structure-from-motion and bundle adjustment algorithms were used to construct a 3-D point cloud, approximate it by a surface mesh, texture it with the back-projected camera frames and draw the corresponding 2-D atlas. Reconstructions of successive sessions were compared; those of the bladder phantom were co-registered, transformed using 3-D thin plate splines and post-processed to highlight significant changes in texture.

Results: The reconstruction algorithms of the presented workflow were able to construct 3-D models and corresponding 2-D atlas of both the in-vivo bladder and the bladder phantom. For the in-vivo bladder the portion of the reconstructed surface area was 58% and 79% for the pre- and post-operative scan, respectively. For the bladder phantom the full surface was reconstructed and the mean reprojection error was 0.081 mm (range 0 mm - 0.79 mm). In inter-session comparison the changes in texture were correctly indicated for all six locations.

Conclusion: The proposed proof-of-concept was able to perform 3-D and 2-D reconstruction of an in-vivo bladder wall based on a set of monocular images. In a phantom study the computer vision algorithms were also effective in co-registering reconstructions of successive sessions and highlighting texture changes between sessions. These techniques may be useful for detecting, monitoring and revisiting suspicious lesions.
Original languageEnglish
JournalInternational journal of computer assisted radiology and surgery
Publication statusAccepted/In press - 31 Mar 2023


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