Facial Wrinkle Segmentation for Cosmetic Dermatology: Pretraining with Texture Map-Based Weak Supervision

Facial wrinkle detection plays a crucial role in cosmetic dermatology. Precise manual segmentation of facial wrinkles is challenging and time-consuming, with inherent subjectivity leading to inconsistent results among graders. To address this issue, we propose two solutions. First, we build and release the first public facial wrinkle dataset, `FFHQ-Wrinkle', an extension of the NVIDIA FFHQ dataset. This dataset includes 1,000 images with human labels and 50,000 images with automatically generated weak labels. This dataset can foster the research community to develop advanced wrinkle detection algorithms. Second, we introduce a training strategy for U-Net-like encoder-decoder models to detect wrinkles across the face automatically. Our method employs a two-stage training strategy: texture map pretraining and finetuning on human-labeled data. Initially, we pretrain models on a large dataset with weak labels (N=50k) or masked texture maps generated through computer vision techniques, without human intervention. Subsequently, we finetune the models using human-labeled data (N=1k), which consists of manually labeled wrinkle masks. During finetuning, the network inputs a combination of RGB and masked texture maps, comprising four channels. We effectively combine labels from multiple annotators to minimize subjectivity in manual labeling. Our strategies demonstrate improved segmentation performance in facial wrinkle segmentation both quantitatively and visually compared to existing pretraining methods.

Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection

1. Research question: With the growing interest in skin diseases and skin aesthetics, the ability to predict facial wrinkles is becoming increasingly important. This study aims to evaluate whether a computational model, convolutional neural networks (CNN), can be trained for automated facial wrinkle segmentation. 2. Findings: Our study presents an effective technique for integrating data from multiple annotators and illustrates that transfer learning can enhance performance, resulting in dependable segmentation of facial wrinkles. 3. Meaning: This approach automates intricate and time-consuming tasks of wrinkle analysis with a deep learning framework. It could be used to facilitate skin treatments and diagnostics.

Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Image Synthesis: T1 MRI to Tau-PET

Alzheimer's Disease (AD) is the most common form of dementia, characterised by cognitive decline and biomarkers such as tau-proteins. Tau-positron emission tomography (tau-PET), which employs a radiotracer to selectively bind, detect, and visualise tau protein aggregates within the brain, is valuable for early AD diagnosis but is less accessible due to high costs, limited availability, and its invasive nature. Image synthesis with neural networks enables the generation of tau-PET images from more accessible T1-weighted magnetic resonance imaging (MRI) images. To ensure high-quality image synthesis, we propose a cyclic 2.5D perceptual loss combined with mean squared error and structural similarity index measure (SSIM) losses. The cyclic 2.5D perceptual loss sequentially calculates the axial 2D average perceptual loss for a specified number of epochs, followed by the coronal and sagittal planes for the same number of epochs. This sequence is cyclically performed, with intervals reducing as the cycles repeat. We conduct supervised synthesis of tau-PET images from T1w MRI images using 516 paired T1w MRI and tau-PET 3D images from the ADNI database. For the collected data, we perform preprocessing, including intensity standardisation for tau-PET images from each manufacturer. The proposed loss, applied to generative 3D U-Net and its variants, outperformed those with 2.5D and 3D perceptual losses in SSIM and peak signal-to-noise ratio (PSNR). In addition, including the cyclic 2.5D perceptual loss to the original losses of GAN-based image synthesis models such as CycleGAN and Pix2Pix improves SSIM and PSNR by at least 2% and 3%. Furthermore, by-manufacturer PET standardisation helps the models in synthesising high-quality images than min-max PET normalisation.