AI 中文总结
该研究扩展了Spinal-Multiple-Myeloma-SEG双能CT数据集，添加腰椎骨小梁分割掩码，支持多发性骨髓瘤相关的骨分析与深度学习任务，已通过TCIA和Zenodo公开。
AI 中文摘要
我们公开了Spinal-Multiple-Myeloma-SEG数据集的扩展版本，该数据集是用于多发性骨髓瘤研究的双能CT成像资源。此数据集的目的是通过添加经专家验证的腰椎骨小梁区域分割结果，实现对椎骨骨微结构的体素级分析。该数据集包含67名成年患者的72次双能CT检查数据（平均年龄66岁，年龄范围48至85岁；女性占比36%），这些数据是通过双层双能CT系统回顾性采集的。数据内容包括常规CT、虚拟单能图像、钙抑制重建图像，以及结构化临床元数据。数据以DICOM格式提供，而分割掩码则以NIfTI和DICOM-SE formats两种格式提供。其主要预期应用包括骨小梁分割、与骨矿物质密度相关的定量分析，以及用于多发性骨髓瘤中椎骨解剖结构和病变骨结构的深度学习模型开发。该数据集支持病理与非病理骨的分割及多模态学习任务。初始骨小梁分割掩码是使用预训练的nnU-Net模型生成的，随后通过专家手动修正和放射学质量控制进行优化，确保解剖结构的一致性。原始数据集可通过TCIA公开获取，而骨小梁分割扩展版本（版本2）已通过Zenodo发布，遵循CC BY 4.0许可协议。Zenodo发布版本可立即公开访问分割掩码，且在整理流程完成后将被纳入官方TCIA数据集。
英文摘要
We present an extension of the publicly available \textit{Spinal-Multiple-Myeloma-SEG} dataset, a dual-energy CT imaging resource for multiple myeloma research. The purpose of this dataset is to enable voxel-wise analysis of vertebral bone microstructure by adding expert-validated segmentation of the trabecular compartment of lumbar vertebrae. The dataset consists of 72 dual-energy CT examinations from 67 adult patients (mean age 66 years, range 48--85; 36\% female), acquired retrospectively using a dual-layer dual-energy CT system. It includes conventional CT, virtual monoenergetic images, and calcium-suppressed reconstructions, along with structured clinical metadata. The data are provided in DICOM format, while segmentation masks are available in both NIfTI and DICOM-SEG formats. The primary intended applications include trabecular bone segmentation, quantitative bone mineral density-related analysis, and development of deep learning models for vertebral anatomy and disease-affected bone structures in multiple myeloma. The dataset supports both segmentation and multimodal learning tasks in pathological and non-pathological bone. Initial trabecular segmentation masks were generated using a pretrained nnU-Net model and subsequently refined through manual expert correction and radiological quality control, ensuring anatomical consistency. The original dataset is publicly available via TCIA (\href{https://doi.org/10.7937/k4qv-hh78}{https://doi.org/10.7937/k4qv-hh78}), while the trabecular segmentation extension (Version 2) is released through Zenodo (\href{https://doi.org/10.5281/zenodo.21628232}{https://doi.org/10.5281/zenodo.21628232}) under the CC BY 4.0 license. The Zenodo release provides immediate public access to the segmentation masks and will be additionally incorporated into the official TCIA collection after completion of the curation process.
Comments8 pages, 2 figures
