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arXiv · 2203.04302

SuperPoint features in endoscopy

O. L. Barbed·F. Chadebecq·J. Morlana·J. M. Martínez-Montiel·A. C. Murillo
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Abstract

There is often a significant gap between research results and applicability in routine medical practice. This work studies the performance of well-known local features on a medical dataset captured during routine colonoscopy procedures. Local feature extraction and matching is a key step for many computer vision applications, specially regarding 3D modelling. In the medical domain, handcrafted local features such as SIFT, with public pipelines such as COLMAP, are still a predominant tool for this kind of tasks. We explore the potential of the well known self-supervised approach SuperPoint, present an adapted variation for the endoscopic domain and propose a challenging evaluation framework. SuperPoint based models achieve significantly higher matching quality than commonly used local features in this domain. Our adapted model avoids features within specularity regions, a frequent and problematic artifact in endoscopic images, with consequent benefits for matching and reconstruction results.

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O. L. Barbed, F. Chadebecq, J. Morlana, J. M. Martínez-Montiel, A. C. Murillo. 2022-03-08. SuperPoint features in endoscopy. https://doi.org/10.1007/978-3-031-21083-9_5

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