Vident-lab: a dataset for multi-task video processing of phantom dental scenes

We introduce a new, asymmetrically annotated dataset of natural teeth in phantom scenes for multi-task video processing: restoration, teeth segmentation, and inter-frame homography estimation. Pairs of frames were acquired with a beam splitter. The dataset constitutes a low-quality frame, its high-quality counterpart, a teeth segmentation mask, and an inter-frame homography matrix. The homography warps the current frame to the previous frame with respect to the teeth. Moreover, we provide the list of human-annotated segmentation masks so that future segmentation methods can adhere to our evaluation protocol and compare their results to MOST-Net in [1]. The remaining segmentation masks were obtained with HRNet48. The dataset has the training, validation, and test sets of 300, 29, and 80 videos, respectively. If you use the Vident-lab dataset, please cite [1] that describes the dataset in detail. [1] Efklidis Katsaros, Piotr K. Ostrowski, Krzysztof Wlodarczak, Emilia Lewandowska, Jacek Ruminski, Damian Siupka-Mroz, Lukasz Lassmann, Anna Jezierska, Daniel Wesierski. "Multi-task video enhancement for dental interventions" In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2022.

本研究提出了一种全新的、面向多任务视频处理的仿真体模场景天然牙齿非对称标注数据集，支持的任务涵盖视频修复、牙齿分割以及帧间单应性估计。该数据集的帧对通过分光镜采集获得。数据集包含低质量帧、对应的高质量帧、牙齿分割掩码以及帧间单应性矩阵。该单应性矩阵可基于牙齿区域将当前帧变换至前一帧的视角。此外，本数据集附带人工标注的分割掩码列表，便于后续分割方法遵循本研究的评估协议，将其结果与文献[1]中的MOST-Net进行对比。其余分割掩码均通过HRNet48模型生成。该数据集的训练集、验证集与测试集分别包含300、29以及80个视频。若您使用Vident-lab数据集，请引用文献[1]以获取该数据集的详细说明。[1] Efklidis Katsaros、Piotr K. Ostrowski、Krzysztof Wlodarczak、Emilia Lewandowska、Jacek Ruminski、Damian Siupka-Mroz、Lukasz Lassmann、Anna Jezierska、Daniel Wesierski. "Multi-task video enhancement for dental interventions"，收录于《国际医学图像计算与计算机辅助干预会议》，Springer Cham出版社，2022年。