用2.5D模型自动识别MRI切片方位，提升诊断准确率
MRI Plane Orientation Detection using a Context-Aware 2.5D Model
- 采用融合多层信息的2.5D上下文模型，避免单切片歧义
- 准确率达99.49%，比2D模型提升60%错误率降低
- 可嵌入临床流程，适合医学影像分析与数据融合场景
人类能轻松识别2D MRI切片中的解剖平面（轴向、冠状、矢状），但自动化系统对此仍存在困难。缺失平面方向元数据会增加分析复杂性，加剧异构数据集间的领域偏移，并降低诊断分类器精度。本文提出一种分类器，可准确生成平面方向元数据。采用2.5D上下文感知模型，利用多切片信息消除孤立切片带来的歧义，实现鲁棒特征学习。在3D切片序列和静态2D图像上训练该模型。尽管2D基准模型准确率为98.74%，2.5D方法进一步提升至99.49%，错误率降低60%。在脑肿瘤检测任务中验证了生成元数据的有效性：基于置信度得分的门控策略选择性使用增强预测，将准确率从仅依赖图像的97.0%提升至98.0%，误诊率下降33.3%。模型已集成至交互式网页应用并开源。
原文摘要 · Abstract (English)
Humans can easily identify anatomical planes (axial, coronal, and sagittal) on a 2D MRI slice, but automated systems struggle with this task. Missing plane orientation metadata can complicate analysis, increase domain shift when merging heterogeneous datasets, and reduce accuracy of diagnostic classifiers. This study develops a classifier that accurately generates plane orientation metadata. We adopt a 2.5D context-aware model that leverages multi-slice information to avoid ambiguity from isolated slices and enable robust feature learning. We train the 2.5D model on both 3D slice sequences and static 2D images. While our 2D reference model achieves 98.74% accuracy, our 2.5D method raises this to 99.49%, reducing errors by 60%, highlighting the importance of 2.5D context. We validate the utility of our generated metadata in a brain tumor detection task. A gated strategy selectively uses metadata-enhanced predictions based on uncertainty scores, boosting accuracy from 97.0% with an image-only model to 98.0%, reducing misdiagnoses by 33.3%. We integrate our plane orientation model into an interactive web application and provide it open-source.
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