发表机构
Massachusetts Institute of Technology; Harvard University; Boston Children’s Hospital; Harvard Medical School（麻省理工学院; 哈佛大学; 波士顿儿童医院; 哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出尺度感知3D深度学习框架，通过融合不同视野的U-Net概率图，提高多模态MRI中脑转移瘤检测的精确度并减少假阳性。
AI 中文摘要
在磁共振成像（MRI）中检测脑转移瘤仍然具有挑战性，因为病灶在大小和外观上差异很大，非常小的转移灶仅占据三维输入的极小部分。我们研究了结合不同的空间视野（FOV）是否能改善多模态MRI中的病灶检测，并提出了一种尺度感知的3D深度学习框架。该方法使用独立训练的$96^3$和$64^3$ 3D U-Net，其全容积概率图通过加权晚期融合进行组合。这种设计使我们能够分别研究空间上下文对图像分辨率和模态选择的影响。在97名患者的开发队列中，跨FOV融合相对于单个模型提高了病灶级别的精确度和F1分数，同时大幅减少了假阳性。同FOV集成对照实验表明，这些增益不能仅通过平均独立训练的网络来解释，支持互补空间上下文的贡献。探索性的跨FOV一致性过滤减少了假阳性，但未改善整体F1分数。这些结果支持跨FOV概率融合作为一种简单且计算上实用的策略，用于改善3D脑转移瘤检测中的精确度-假阳性权衡。
英文摘要
Detecting brain metastases in magnetic resonance imaging (MRI) remains challenging because lesions vary widely in size and appearance, with very small metastases occupying only a minute fraction of a three-dimensional input. We investigate whether combining different spatial fields of view (FOVs) improves lesion detection in multimodal MRI and present a scale-aware 3D deep-learning framework. The method uses independently trained $96^3$ and $64^3$ 3D U-Nets whose whole-volume probability maps are combined by weighted late fusion. This design allows us to study the effect of spatial context separately from image resolution and modality choice. On a 97-patient development cohort, cross-FOV fusion improved lesion-level precision and F1 while substantially reducing false positives relative to the individual models. A same-FOV ensemble control showed that these gains were not explained solely by averaging independently trained networks, supporting a contribution from complementary spatial context. An exploratory cross-FOV agreement filter reduced false positives but did not improve overall F1. These results support cross-FOV probability fusion as a simple and computationally practical strategy for improving the precision-false-positive trade-off in 3D brain-metastasis detection.
Comments12 pages, 3 figures, 3 tables
Journal refProceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2026), Strasbourg, Sep 2026
