Abstract
Detecting and delineating tiny targets in 3D brain scans is a central yet under-addressed challenge in medical this http URL ischemic stroke, for instance, the culprit thrombus is small, low-contrast, and variably expressed across modalities(e.g., susceptibility-weighted T2 blooming, diffusion restriction on DWI/ADC), while real-world multi-center dataintroduce domain shifts, anisotropy, and frequent missing sequences. We introduce a methodology that couples an attention-based recurrent segmentation network (UpAttLLSTM), a training schedule that progressively increases the difficulty of hetero-modal learning, with gradual modality dropout, UpAttLLSTM aggregates context across slices via recurrent units (2.5D) and uses attention gates to fuse complementary cues across available sequences, making it robust to anisotropy and class imbalance. Gradual modality dropout systematically simulates site heterogeneity,noise, and missing modalities during training, acting as both augmentation and regularization to improve multi-center generalization. On a monocentric cohort, our approach detects thrombi in >90% of cases with a Dice score of 0.65. In a multi-center setting with missing modalities, it achieves-80% detection with a Dice score around 0.35. Beyond stroke, the proposed methodology directly transfers to other small-lesion tasks in 3D medical imaging where targets are scarce, subtle, and modality-dependent
Abstract (translated)
在3D脑部扫描中检测并勾画微小目标，是医学影像领域一个核心却尚未得到充分解决的技术挑战。以缺血性卒中为例，致病血栓体积小、对比度低，且在不同模态影像中表现各异（例如磁敏感加权T2序列的 blooming 效应、DWI/ADC序列上的弥散受限）。而真实世界中的多中心数据会引入域偏移、各向异性以及序列频繁缺失等问题。本研究提出一种方法论，将基于注意力的循环分割网络（UpAttLLSTM）与渐进式异模态学习训练策略相结合，后者通过逐步增加模态缺失的难度实现数据增强与正则化。UpAttLLSTM 通过循环单元（2.5D）跨切片聚合上下文信息，并利用注意力门控机制融合多序列间的互补特征，从而对数据各向异性及类别不平衡问题具有鲁棒性。渐进式模态缺失策略在训练阶段系统模拟中心异质性、噪声及模态缺失情况，作为数据增强与正则化手段提升多中心泛化能力。在单中心队列中，该方法对血栓的检测率超过90%，Dice系数达0.65；在多中心模态缺失场景下，仍可实现80%的检测率，Dice系数约0.35。除卒中领域外，该方法论可直接迁移至其他3D医学影像中的小病灶任务——这些目标普遍存在稀缺性、隐蔽性及模态依赖性特征。
URL
https://arxiv.org/abs/2604.00817
