Synthetic skin image generation using a physics-based, object-to-image computational pipeline.

Elena Sizikova, Niloufar Saharkhiz, Andrea Kim, Miguel Lago, Jana G Delfino, Aldo Badano

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Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Elena SizikovaU.S. Food and Drug Administration, Silver Spring, MD, USA. elena.sizikova@fda.hhs.gov.ORCID http://orcid.org/0009-0007-4422-6801

Niloufar SaharkhizU.S. Food and Drug Administration, Silver Spring, MD, USA.

Andrea KimU.S. Food and Drug Administration, Silver Spring, MD, USA.

Miguel LagoU.S. Food and Drug Administration, Silver Spring, MD, USA.

Jana G DelfinoU.S. Food and Drug Administration, Silver Spring, MD, USA.

Aldo BadanoU.S. Food and Drug Administration, Silver Spring, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe describe an extended overview and evaluation of the S-SYNTH skin simulation approach (Kim et al. in MICCAI, Springer, pp 734-744, 2024), a knowledge-based skin simulation approach to generate synthetic skin images for aiding artificial intelligence (AI) algorithm development.

methodsThe skin object model allows for controlled variation in skin appearance, including skin color, presence of hair, lesion shape, and blood fraction. Using this approach, we study the effect of model variations on AI models for skin lesion segmentation.

resultsResults obtained using synthetic data follow similar comparative trends as patient dermatologic images and have the potential to mitigate biases and limitations of existing patient datasets including small dataset size and poor representativeness. We also demonstrate a novel use case where S-SYNTH can enhance diffusion model performance for dermatological applications.

conclusionSynthetic images have the ability to address many limitations of available patient datasets in skin imaging. Simulating datasets allows for tight control over the parameter space and the ability to create rare examples and annotations that are typically missing from patient datasets.

Indexed as

Artificial IntelligenceComputer SimulationImage Processing, Computer-AssistedSkinDatasets as TopicHumansDermatologyMedical imagingSimulationSynthetic data

Identifiers

PMID41801592

DOI10.1007/s11548-026-03587-2

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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.