Evidence map›Paper›PMID 30774651›Full record

ArticleInternational journal of biomedical imaging2019

Measuring Surface Area of Skin Lesions with 2D and 3D Algorithms.

Houman Mirzaalian Dastjerdi, Dominique Töpfer, Stefan J Rupitsch, Andreas Maier

Abstract read
In one paragraph

Article in International journal of biomedical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. RulerNet: Learning perspective-invariant ruler representations for robust image scale estimation.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2026
    Article
  3. Article
  4. Article
  5. Extraction of Ruler Markings For Estimating Physical Size of Oral Lesions.Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition · 2022
    Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Houman Mirzaalian DastjerdiFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Lehrstuhl für Informatik 5 (Mustererkennung), Germany.ORCID 0000-0002-0724-3158
Dominique TöpferSoftgate gmbh, Erlangen, Germany.
Stefan J RupitschFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Lehrstuhl für Sensorik (LSE), Germany.
Andreas MaierFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Lehrstuhl für Informatik 5 (Mustererkennung), Germany.ORCID 0000-0002-9550-5284

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe treatment of skin lesions of various kinds is a common task in clinical routine. Apart from wound care, the assessment of treatment efficacy plays an important role. In this paper, we present a new approach to measure the skin lesion surface in two and three dimensions.

methodsFor the 2D approach, a single photo containing a flexible paper ruler is taken. After semi-automatic segmentation of the lesion, evaluation is based on local scale estimation using the ruler. For the 3D approach, reconstruction is based on Structure from Motion. Roughly outlining the region of interest around the lesion is required for both methods.

resultsThe measurement evaluation was performed on 117 phantom images and five phantom videos for 2D and 3D approach, respectively. We found an absolute error of 0.99±1.18  cm

conclusionsWe developed a new wound segmentation and surface area measurement technique for skin lesions even on a bent surface. The 2D technique provides the user with a fast, user-friendly segmentation and measurement tool with reasonable accuracy for home care assessment of treatment. For 3D only preliminary results could be provided. Measurements were only based on phantoms and have to be repeated with real clinical data.

Identifiers

PMID30774651
PMCPMC6350543

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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.