Deep learning; Dermatology; Differentiable rendering; Lesion detection; Lesion segmentation; Skin image analysis; Synthesis; Humans; Imaging, Three-Dimensional/methods; Deep Learning; Image Interpretation, Computer-Assisted/methods; Skin Diseases/diagnostic imaging; 2D images; Dermatological images; Image-analysis; Lesion segmentations; Lighting conditions; Skin image analyse; Skin images; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Skin Diseases; Radiological and Ultrasound Technology; Radiology, Nuclear Medicine and Imaging; Computer Vision and Pattern Recognition; Health Informatics; Computer Graphics and Computer-Aided Design
Abstract :
[en] In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis. However, existing datasets in this domain have significant limitations, including a small number of image samples, limited disease conditions, insufficient annotations, and non-standardized image acquisitions. To address these shortcomings, we propose a novel framework called DermSynth3D. DermSynth3D blends skin disease patterns onto 3D textured meshes of human subjects using a differentiable renderer and generates 2D images from various camera viewpoints under chosen lighting conditions in diverse background scenes. Our method adheres to top-down rules that constrain the blending and rendering process to create 2D images with skin conditions that mimic in-the-wild acquisitions, ensuring more meaningful results. The framework generates photo-realistic 2D dermatological images and the corresponding dense annotations for semantic segmentation of the skin, skin conditions, body parts, bounding boxes around lesions, depth maps, and other 3D scene parameters, such as camera position and lighting conditions. DermSynth3D allows for the creation of custom datasets for various dermatology tasks. We demonstrate the effectiveness of data generated using DermSynth3D by training DL models on synthetic data and evaluating them on various dermatology tasks using real 2D dermatological images. We make our code publicly available at https://github.com/sfu-mial/DermSynth3D.
Disciplines :
Computer science
Author, co-author :
Sinha, Ashish ; Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby V5A 1S6, Canada
Kawahara, Jeremy ; Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby V5A 1S6, Canada
Pakzad, Arezou ; Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby V5A 1S6, Canada
Abhishek, Kumar ; Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby V5A 1S6, Canada
RUTHVEN, Matthieu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust > CVI2 > Team Djamila AOUADA
GHORBEL, Enjie ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust > CVI2 > Team Djamila AOUADA
KACEM, Anis ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
AOUADA, Djamila ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
Hamarneh, Ghassan ; Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby V5A 1S6, Canada. Electronic address: hamarneh@sfu.ca
External co-authors :
yes
Language :
English
Title :
DermSynth3D: Synthesis of in-the-wild annotated dermatology images.
Natural Sciences and Engineering Research Council of Canada Fonds National de la Recherche Luxembourg BC Cancer Foundation Nvidia Alliance de recherche numérique du Canada
Funding text :
This research was enabled in part by support provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2020-06752 , the BC Cancer Foundation - BrainCare BC Fund , Luxembourg National Research Fund (FNR) project BRIDGES2021/IS/16353350/FaKeDeTeR, and the computational resources provided by WestGrid (Cedar) , Digital Research Alliance of Canada , and NVIDIA Corporation . The authors are also grateful to Megan Andrews and Colin Li for their assistance with the data annotation efforts, which included the manual segmentations of non-skin regions in the texture images.
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