LSS MRI AISSLab Dataset: Medical Spine Sagittal MRI Dataset for Segmentation and Foraminal Stenosis detection

☐ Dataset: ● The LSS MRI AISSLab Dataset is a comprehensive sagittal lumbar spine MRI collection containing 500 patients with fully curated imaging data, expert clinical annotations, and detailed anatomical segmentation masks for the scientifc purpose (noncommercial). ● This dataset was approved by the Institutional Review Board (IRB) and clinically validated by neurosurgeons from the Fırat University Non-Interventional Research Ethics Committee (session number: 2023/12-20; session date: 14.09.2023). The dataset consists of 8,500 sagittal lumbar spine MRI slices and 2,979 expert-verified bounding-box annotations describing foraminal stenosis across the five lumbar levels (L1–L2 through L5–S1). ● A total of 1,396 right foraminal stenosis (RFS) and 1,583 left foraminal stenosis (LFS) regions were annotated. Each annotation specifies the lumbar level, anatomical side, and clinically assigned stenosis grade (Normal, Mild, Moderate, Severe). ● The stenosis severity distribution demonstrates a predominance of early-stage findings, consisting of Normal (67.45%), Mild (17.06%), Moderate (8.53%), and Severe (6.99%) cases. ● The dataset provides also the expert-refined segmentation masks on the middle sagittal slice for key anatomical structures, including vertebrae, intervertebral discs (IVDs), sacrum, posterior A, posterior B, and the anterior background region. These masks were generated through a combined automated-and-manual refinement workflow and reviewed by neurologists to ensure anatomical accuracy. ● The dataset is organized into three components: (1) full sagittal DICOM series for each patient; (2) middle-slice images accompanied by pixel-level masks in PNG/XML format; and (3) full-slice PNG images with corresponding stenosis annotations when visible. ☐ ACKNOWLEDGMENT: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2023-00256517) and by the TUBITAK (The Scientific and Technological research Council of Turkey) under Grant Number: 123N325. This work was supported by the IITP(Institute of Information & Communications Technology Planning & Evaluation)-ITRC(Information Technology Research Center) grant funded by the Korea government (Ministry of Science and ICT) (IITP-2025-RS-2024-00437191). ☐ Please cite these articles: [1] Salem, Saied, Afnan Habib, Mukhlis Raza, Zaid Al-Huda, Omar Al-maqtari, Bilal Ertuğrul, Özal Yıldırım, Yeong Hyeon Gu, and Mugahed A. Al-antari. "AutoSpineAI: Lightweight Multimodal CAD Framework for Lumbar Spine MRI Assessments." In IEEE-EMBS International Conference on Biomedical and Health Informatics 2025. [2] Al-Antari, Mugahed A., Saied Salem, Mukhlis Raza, Ahmed S. Elbadawy, Ertan Bütün, Ahmet Arif Aydin, Murat Aydoğan, Bilal Ertuğrul, Muhammed Talo, and Yeong Hyeon Gu. "Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review." Artificial Intelligence Review 58, no. 8 (2025): 221.### 数据集 ● LSS MRI AISSLab数据集是一套全面的矢状位腰椎磁共振成像（MRI）集合，涵盖500名患者的经过完整整理的影像数据、专家临床标注，以及用于非商业科研用途的详细解剖分割掩码。 ● 本数据集已获得机构审查委员会（Institutional Review Board, IRB）批准，并由菲拉特大学非介入研究伦理委员会的神经外科医生完成临床验证（会议编号：2023/12-20；会议日期：2023年9月14日）。数据集包含8500张矢状位腰椎MRI切片，以及2979份经专家核验的边界框标注，用于描述5个腰椎节段（L1~L2至L5~S1）的椎间孔狭窄情况。 ● 共标注了1396个右侧椎间孔狭窄（Right Foraminal Stenosis, RFS）区域与1583个左侧椎间孔狭窄（Left Foraminal Stenosis, LFS）区域。每份标注均明确了腰椎节段、解剖侧别以及临床评定的狭窄等级：正常（Normal）、轻度（Mild）、中度（Moderate）、重度（Severe）。 ● 狭窄严重程度分布显示以早期病变为主，其中正常占比67.45%、轻度占17.06%、中度占8.53%、重度占6.99%。 ● 本数据集还提供了针对关键解剖结构的正中矢状位切片专家精修分割掩码，涵盖椎体、椎间盘（Intervertebral Discs, IVDs）、骶骨、后结构A、后结构B以及前部背景区域。上述掩码通过自动化与人工结合的精修流程生成，并经神经科医生审核以确保解剖准确性。 ● 本数据集分为三个组件：(1) 每名患者的完整矢状位DICOM序列；(2) 附带PNG/XML格式像素级掩码的正中切片图像；(3) 带有对应可见切片狭窄标注的全切片PNG图像。 ### 致谢 本研究获得韩国国家研究基金会（National Research Foundation of Korea, NRF）由韩国政府（科学和信息通信技术部，MSIT）资助的项目（编号：RS-2023-00256517），以及土耳其科学和技术研究理事会（The Scientific and Technological Research Council of Turkey, TUBITAK）编号为123N325的项目资助。本研究同时获得韩国政府（科学和信息通信技术部）资助的韩国信息与通信技术规划评估院（Institute of Information & Communications Technology Planning & Evaluation, IITP）-信息技术研究中心（Information Technology Research Center, ITRC）项目（编号：IITP-2025-RS-2024-00437191）支持。 ### 请引用以下文献 [1] Salem, Saied, Afnan Habib, Mukhlis Raza, Zaid Al-Huda, Omar Al-maqtari, Bilal Ertuğrul, Özal Yıldırım, Yeong Hyeon Gu, Mugahed A. Al-antari. AutoSpineAI：用于腰椎MRI评估的轻量级多模态计算机辅助诊断（CAD）框架 // 2025年IEEE-EMBS生物医学与健康信息学国际会议. [2] Al-Antari, Mugahed A., Saied Salem, Mukhlis Raza, Ahmed S. Elbadawy, Ertan Bütün, Ahmet Arif Aydin, Murat Aydoğan, Bilal Ertuğrul, Muhammed Talo, Yeong Hyeon Gu. 人工智能辅助腰椎椎管狭窄MRI预测方案评估：系统综述 // 《人工智能评论》, 2025, 58(8): 221.