公开脊柱多发性骨髓瘤影像数据集，新增腰椎松质骨精细分割标注。
Spinal-Multiple-Myeloma-SEG: A Dual-Energy CT Dataset Extended with Trabecular Bone Segmentation of Lumbar Vertebrae
- 基于双能CT图像，用预训练nnU-Net生成松质骨分割初稿。
- 经专家手动修正与放射科质控，实现72例患者腰椎松质骨精确标注。
- 适合做骨骼微结构分析、骨密度定量及多发性骨髓瘤深度学习研究。
我们扩展了公开的《Spinal-Multiple-Myeloma-SEG》数据集，该数据集为多发性骨髓瘤研究提供双能CT影像资源。本扩展旨在通过添加腰椎松质骨的专家验证分割，实现椎体骨微结构的体素级分析。数据集包含67名成人患者（平均年龄66岁，范围48–85；36%女性）的72例回顾性双层双能CT扫描，采用双能系统采集，包含常规CT、虚拟单能成像和钙抑制重建图像，以及结构化临床元数据。数据以DICOM格式提供，分割掩码同时支持NIfTI和DICOM-SEG格式。主要应用包括松质骨分割、与骨矿物质密度相关的定量分析，以及针对多发性骨髓瘤病理性骨结构的深度学习模型开发。初始分割由预训练nnU-Net生成，后经专家手动修正与放射科质量控制，确保解剖一致性。原始数据集可通过TCIA公开获取（https://doi.org/10.7937/k4qv-hh78），而本扩展版本（第2版）已通过Zenodo发布（https://doi.org/10.5281/zenodo.21628232），采用CC BY 4.0许可，可立即访问分割掩码，并将在完成审核后整合进官方TCIA库。
原文摘要 · Abstract (English)
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.
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