Evidence map›Paper›PMID 37085675›Full record

ArticleInternational journal of computer assisted radiology and surgery2023

3-D and 2-D reconstruction of bladders for the assessment of inter-session detection of tissue changes: a proof of concept.

Vincent Groenhuis, Antonius G de Groot, Erik B Cornel, Stefano Stramigioli, Françoise J Siepel

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Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Vincent GroenhuisRobotics and Mechatronics, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands. v.groenhuis@utwente.nl.ORCID http://orcid.org/0000-0002-1073-6690
Antonius G de GrootRobotics and Mechatronics, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands.
Erik B CornelDepartment of Urology, Ziekenhuisgroep Twente (ZGT), Zilvermeeuw 1, 7609 PP, Almelo, The Netherlands.
Stefano StramigioliRobotics and Mechatronics, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands.
Françoise J SiepelRobotics and Mechatronics, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands.

Funding

OPOost and European Union PROJ-00970
6 · The paper itself

Abstract

purposeAbnormalities 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.

methodsVideos 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.

resultsThe 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 0-0.79 mm). In inter-session comparison the changes in texture were correctly indicated for all six locations.

conclusionThe 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.

Indexed as

AlgorithmsUrinary BladderHumansMotionPhantoms, Imaging3-D reconstructionBladderComputer visionMonocularProof of concept

Identifiers

PMID37085675
PMCPMC10497453

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.