用物理仿真生成乳腺影像数据，解决标注难问题
T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images
- 通过物理模拟生成带像素级分割标签的合成乳腺图像
- 构建了包含2D和3D乳腺影像的大型开源数据集
- 适合用于医疗影像算法训练与验证
医学影像算法发展受限于大规模、高质量标注数据的获取。利用符合真实生物物理规律的合成数据可缓解这一困境。本文提出基于物理仿真的方法，生成带有像素级分割标注的合成乳腺影像。具体针对乳腺成像分析，发布了T-SYNTH数据集，包含成对的2D数字乳腺摄影（DM）与3D数字乳腺断层扫描（DBT）图像。初步实验表明，T-SYNTH图像在增强有限真实患者数据方面对DM和DBT检测任务具有潜力。数据与代码已公开于https://github.com/DIDSR/tsynth-release。
原文摘要 · Abstract (English)
One of the key impediments for developing and assessing robust medical imaging algorithms is limited access to large-scale datasets with suitable annotations. Synthetic data generated with plausible physical and biological constraints may address some of these data limitations. We propose the use of physics simulations to generate synthetic images with pixel-level segmentation annotations, which are notoriously difficult to obtain. Specifically, we apply this approach to breast imaging analysis and release T-SYNTH, a large-scale open-source dataset of paired 2D digital mammography (DM) and 3D digital breast tomosynthesis (DBT) images. Our initial experimental results indicate that T-SYNTH images show promise for augmenting limited real patient datasets for detection tasks in DM and DBT. Our data and code are publicly available at https://github.com/DIDSR/tsynth-release.
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