Concetti Chiave
Proposing DermSynth3D for generating synthetic 2D skin image datasets using 3D human body meshes blended with skin disorders from clinical images.
Sintesi
The content introduces DermSynth3D, a novel framework for synthesizing dermatology images. It addresses the limitations of existing datasets by blending skin disease patterns onto 3D textured meshes to create realistic 2D dermoscopy images. The framework generates dense annotations for various dermatological tasks and allows for custom dataset creation. The process involves placing and blending skin conditions into the mesh, rendering 2D views, and creating a dataset. Experimental details include wound bounding box detection, lesion segmentation, and evaluation on real images.
- Introduction to DermSynth3D framework for synthetic dermatology image generation.
- Addressing limitations of existing datasets through blending skin disease patterns onto 3D textured meshes.
- Process involving placement and blending of skin conditions, rendering 2D views, and dataset creation.
- Experimental details on wound bounding box detection, lesion segmentation, and evaluation on real images.
Statistiche
"We use the FUSeg dataset from The Foot Ulcer Segmentation Challenge [84], which contains standard training, validation, and testing partitions."
"For the wound detection task, we convert masks of wounds to bounding boxes by labeling connected regions."
"We train a DeepLabV3 network with a ResNet-50 backbone for wound segmentation."
Citazioni
"We propose DermSynth3D as a computational pipeline along with an open-source software library for generating synthetic 2D skin image datasets."
"Our approach uses a differentiable renderer to blend skin lesions within the texture image of the 3D human body."
"The modular design of our DermSynth3D pipeline allows easy modification of settings for photo-realistic rendering."