DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation
Abstract Optical coherence tomography angiography (OCTA) has emerged as a promising tool for non-invasive vascular imaging in dermatology. However, the field lacks standardized methods for processing and analyzing these complex images, as well as sufficient annotated datasets for developing automate...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05763-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849226659805265920 |
|---|---|
| author | Giulia Rotunno Massimo Salvi Julia Deinsberger Lisa Krainz Benedikt Weber Christoph Sinz Harald Kittler Leopold Schmetterer Wolfgang Drexler Mengyang Liu Kristen M. Meiburger |
| author_facet | Giulia Rotunno Massimo Salvi Julia Deinsberger Lisa Krainz Benedikt Weber Christoph Sinz Harald Kittler Leopold Schmetterer Wolfgang Drexler Mengyang Liu Kristen M. Meiburger |
| author_sort | Giulia Rotunno |
| collection | DOAJ |
| description | Abstract Optical coherence tomography angiography (OCTA) has emerged as a promising tool for non-invasive vascular imaging in dermatology. However, the field lacks standardized methods for processing and analyzing these complex images, as well as sufficient annotated datasets for developing automated analysis tools. We present DERMA-OCTA, the first open-access dermatological OCTA dataset, comprising 330 volumetric scans from 74 subjects with various skin conditions. The dataset contains the original 2D and 3D OCTA acquisitions, as well as versions processed with five different preprocessing methods, and the reference 2D and 3D segmentations. For each version, segmentation labels are provided, generated using the U-Net architecture as 2D and 3D segmentation approaches. By providing high-resolution, annotated OCTA data across a range of skin pathologies, this dataset offers a valuable resource for training deep learning models, benchmarking segmentation algorithms, and facilitating research into non-invasive skin imaging. The DERMA-OCTA dataset is freely downloadable. |
| format | Article |
| id | doaj-art-9184fc9eadd24ff8b45087f311e13a3a |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-9184fc9eadd24ff8b45087f311e13a3a2025-08-24T11:07:14ZengNature PortfolioScientific Data2052-44632025-08-0112111010.1038/s41597-025-05763-6DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel SegmentationGiulia Rotunno0Massimo Salvi1Julia Deinsberger2Lisa Krainz3Benedikt Weber4Christoph Sinz5Harald Kittler6Leopold Schmetterer7Wolfgang Drexler8Mengyang Liu9Kristen M. Meiburger10PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di TorinoPolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di TorinoDepartment of Dermatology, Medical University of ViennaCenter for Medical Physics and Biomedical Engineering, Medical University of ViennaDepartment of Dermatology, Medical University of ViennaMelanoma Institute Australia, The University of SydneyDepartment of Dermatology, Medical University of ViennaCenter for Medical Physics and Biomedical Engineering, Medical University of ViennaCenter for Medical Physics and Biomedical Engineering, Medical University of ViennaCenter for Medical Physics and Biomedical Engineering, Medical University of ViennaPolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di TorinoAbstract Optical coherence tomography angiography (OCTA) has emerged as a promising tool for non-invasive vascular imaging in dermatology. However, the field lacks standardized methods for processing and analyzing these complex images, as well as sufficient annotated datasets for developing automated analysis tools. We present DERMA-OCTA, the first open-access dermatological OCTA dataset, comprising 330 volumetric scans from 74 subjects with various skin conditions. The dataset contains the original 2D and 3D OCTA acquisitions, as well as versions processed with five different preprocessing methods, and the reference 2D and 3D segmentations. For each version, segmentation labels are provided, generated using the U-Net architecture as 2D and 3D segmentation approaches. By providing high-resolution, annotated OCTA data across a range of skin pathologies, this dataset offers a valuable resource for training deep learning models, benchmarking segmentation algorithms, and facilitating research into non-invasive skin imaging. The DERMA-OCTA dataset is freely downloadable.https://doi.org/10.1038/s41597-025-05763-6 |
| spellingShingle | Giulia Rotunno Massimo Salvi Julia Deinsberger Lisa Krainz Benedikt Weber Christoph Sinz Harald Kittler Leopold Schmetterer Wolfgang Drexler Mengyang Liu Kristen M. Meiburger DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation Scientific Data |
| title | DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation |
| title_full | DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation |
| title_fullStr | DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation |
| title_full_unstemmed | DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation |
| title_short | DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation |
| title_sort | derma octa a comprehensive dataset and preprocessing pipeline for dermatological octa vessel segmentation |
| url | https://doi.org/10.1038/s41597-025-05763-6 |
| work_keys_str_mv | AT giuliarotunno dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation AT massimosalvi dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation AT juliadeinsberger dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation AT lisakrainz dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation AT benediktweber dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation AT christophsinz dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation AT haraldkittler dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation AT leopoldschmetterer dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation AT wolfgangdrexler dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation AT mengyangliu dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation AT kristenmmeiburger dermaoctaacomprehensivedatasetandpreprocessingpipelinefordermatologicaloctavesselsegmentation |