NeuroCBIR：一种用于全脑和区域特异性MRI的快速准确图像检索系统

中文摘要 AI 辅助

神经影像中的基于内容的图像检索（CBIR）能够识别结构相似的脑部扫描，支持诊断、预后和治疗规划；然而，现有方法通常局限于小数据集、单一脑区或粗略的类别标签，从而限制了其临床实用性和泛化能力。在此，我们提出了NeuroCBIR，一个用于快速灵活检索全脑和区域特异性3D T1w MRI扫描的框架。共提取了103个皮层和皮层下区域，以实现全脑和区域级别的查询。NeuroCBIR利用变分自编码器（VAE）结合对比学习学习到的潜在表示，生成捕获解剖模式的扫描特定嵌入。这些嵌入在受试者重新识别、零样本年龄预测和零样本多类病理分层中进行了评估。重新识别性能在全脑和脑区级别均较高（前5个检索图像的平均平均精度（mAP@5）≥98.4%），并在数据集和采集条件中具有稳健的泛化能力。虽然NeuroCBIR未针对年龄预测或病理分层进行训练，但这两项任务的零样本评估表明，嵌入编码了用于下游任务的有意义信息。在4核CPU上进行嵌入提取每个扫描约需18.7秒，而相似性搜索几乎即时完成（小于0.01秒）。NeuroCBIR公开可用于脑MRI，包含超过26,000个预计算的T1w MRI嵌入。它支持可重复研究、区域特异性灵活性以及临床上有意义的个性化诊断支持。软件可在以下网址获取：https URL。

英文摘要

Content-based image retrieval (CBIR) in neuroimaging enables the identification of structurally similar brain scans, supporting diagnosis, prognosis, and treatment planning; however, existing methods are often limited to small datasets, single brain regions, or coarse class labels, thereby restricting their clinical utility and generalizability. Here, we present NeuroCBIR, a framework for fast and flexible retrieval of both whole-brain and region-specific 3D T1w MRI scans. A total of 103 cortical and subcortical regions are extracted to enable both whole-brain and region-level queries. NeuroCBIR leverages latent representations learned by a variational autoencoder (VAE) combined with contrastive learning, producing scan-specific embeddings that capture anatomical patterns. These embeddings were evaluated for subject re-identification, zero-shot age prediction, and zero-shot multi-class pathology stratification. Re-identification performance was high across both whole-brain and brain-region levels (mean average precision across the top-5 retrieved images (mAP@5) >= 98.4%), with robust generalization across datasets and acquisition conditions. While NeuroCBIR is not trained for age prediction or pathology stratification, zero-shot evaluations for these two tasks demonstrate that the embeddings encode meaningful information for downstream tasks. Embedding extraction on a 4-core CPU required approximately 18.7 s per scan, whereas similarity search was effectively instantaneous (less than 0.01 s). NeuroCBIR is publicly available for brain MRI with more than 26,000 precomputed T1w MRI embeddings. It supports reproducible research, region-specific flexibility, and clinically meaningful personalized diagnostic support. The software is available at https://github.com/minnelab/NeuroCBIR.

发表机构

• KTH Royal Institute of Technology（瑞典皇家理工学院）
• Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）
• Karolinska Institute（卡罗林斯卡学院）
• Universidad Fernando Pessoa Canarias（费尔南多·佩索阿大学加那利分校）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Neuroimaging, Content-Based Image Retrieval, MRI, Zero-Shot Learning