Junho Moon

Junho Moon, Haejun Chung, Ikbeom Jang

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. It includes 1,000 images with human labels and 50,000 images with automatically generated weak labels. This dataset could serve as a foundation for the research community to develop advanced wrinkle detection algorithms. Second, we introduce a simple training strategy utilizing texture maps, applicable to various segmentation models, to detect wrinkles across the face. Our two-stage training strategy first pretrain models on a large dataset with weak labels (N=50k), or masked texture maps generated through computer vision techniques, without human intervention. We then finetune the models using human-labeled data (N=1k), which consists of manually labeled wrinkle masks. The network takes as input a combination of RGB and masked texture map of the image, comprising four channels, in finetuning. 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. The dataset is available at https://github.com/labhai/ffhq-wrinkle-dataset.

Ik Jun Moon, Junho Moon, Ikbeom Jang

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.

Junho Moon, Symac Kim, Haejun Chung et al.

There is a demand for medical image synthesis or translation to generate synthetic images of missing modalities from available data. This need stems from challenges such as restricted access to high-cost imaging devices, government regulations, or failure to follow up with patients or study participants. In medical imaging, preserving high-level semantic features is often more critical than achieving pixel-level accuracy. Perceptual loss functions are widely employed to train medical image synthesis or translation models, as they quantify differences in high-level image features using a pre-trained feature extraction network. While 3D and 2.5D perceptual losses are used in 3D medical image synthesis, they face challenges, such as the lack of pre-trained 3D models or difficulties in balancing loss reduction across different planes. In this work, we focus on synthesizing 3D tau PET images from 3D T1-weighted MR images. We propose a cyclic 2.5D perceptual loss that sequentially computes the 2D average perceptual loss for each of the axial, coronal, and sagittal planes over epochs, with the cycle duration gradually decreasing. Additionally, we process tau PET images using by-manufacturer standardization to enhance the preservation of high-SUVR regions indicative of tau pathology and mitigate SUVR variability caused by inter-manufacturer differences. We combine the proposed loss with SSIM and MSE losses and demonstrate its effectiveness in improving both quantitative and qualitative performance across various generative models, including U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix.