mlsanityData to 13 Oct 2026

Skin Lesion Correspondence Localization in Total Body Photography

arXiv 2307.09642 ↗First posted 18 Jul 2023cs.CVcited by 2

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Abstract (arXiv)

Longitudinal tracking of skin lesions - finding correspondence, changes in morphology, and texture - is beneficial to the early detection of melanoma. However, it has not been well investigated in the context of full-body imaging. We propose a novel framework combining geometric and texture information to localize skin lesion correspondence from a source scan to a target scan in total body photography (TBP). Body landmarks or sparse correspondence are first created on the source and target 3D textured meshes. Every vertex on each of the meshes is then mapped to a feature vector characterizing the geodesic distances to the landmarks on that mesh. Then, for each lesion of interest (LOI) on the source, its corresponding location on the target is first coarsely estimated using the geometric information encoded in the feature vectors and then refined using the texture information. We evaluated the framework quantitatively on both a public and a private dataset, for which our success rates (at 10 mm criterion) are comparable to the only reported longitudinal study. As full-body 3D capture becomes more prevalent and has higher quality, we expect the proposed method to constitute a valuable step in the longitudinal tracking of skin lesions.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 4 of its 28 references that are in the map. First posted on arXiv 18 Jul 2023.

Builds on

4 of 28 references.

References · 4

  1. 2013Rotational Projection Statistics for 3D Local Surface Description and Object Recognitioncited by 36
  2. 2021Deep Virtual Markers for Articulated 3D Shapescited by 7
  3. 2016Skin Cancer Detection and Tracking using Data Synthesis and Deep Learningcited by 6
  4. 2021Skin3D: Detection and Longitudinal Tracking of Pigmented Skin Lesions in 3D Total-Body Textured Meshescited by 6

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.