发表机构
University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对造影剂增强MRI分割精度下降的问题，提出空间特征线性调制（SpFiLM）层，在UNet中按空间变化调制特征，使统一网络可靠分割注射前后T1w MRI，平均Dice从80.2%提升至84.1%。
AI 中文摘要
大多数自动脑部分割工具是在T1加权（T1w）MRI上开发和验证的。然而，一些与分割相关的临床工作流程仅使用对比增强T1w（T1ce）MRI，而T1w训练的模型在这些图像上准确性较低。我们提出了一个统一网络，能够可靠地分割造影剂注射前和注射后的T1w MRI，该网络在两者的组合上进行训练，并通过条件调制使其响应在空间上对不同类型图像有所区别。特征线性调制（FiLM）是一种已知的基于输入的网络调制方法，它对输入均匀地应用逐通道的缩放和偏移。然而，造影剂注射前后外观变化在大脑局部区域有所不同，这使得FiLM在我们的应用场景中并非最优。在本工作中，我们引入了空间FiLM（SpFiLM），这是一种条件调制层，其调制在空间上变化，从图像衍生的空间模式中组装逐体素的缩放和偏移。使用134名患者的配对T1w和T1ce MRI队列，分割为106个类别，在UNet中添加SpFiLM层将25名患者测试集上的平均Dice系数从80.2%提高到84.1%，相对提升了4.9%。添加SpFiLM层在造影剂注射前和注射后的MRI上均取得了最佳性能，即使在控制网络参数数量时也是如此。
英文摘要
Most automated brain parcellation tools are developed and validated on T1-weighted (T1w) MRI. Yet, some clinical workflows for which parcellation is relevant only use contrast-enhanced T1w (T1ce) MRI, on which T1w-trained models are less accurate. We present a unified network that parcellates both pre- and post-contrast agent T1w MRI reliably, trained on a combination of the two with conditioning that spatially modulates its response differently for each. Feature-wise Linear Modulation (FiLM) is a known approach for input-based modulation in networks. It applies a per-channel scale and shift uniformly across the input. However, the appearance change between pre- and post-contrast varies locally across the brain, making FiLM suboptimal for our use case. In this work, we introduce Spatial FiLM (SpFiLM), a conditioning layer whose modulation varies spatially, assembling a voxel-wise scale and shift from image-derived spatial patterns. Using a cohort of 134 patients with paired T1w and T1ce MRI parcellated into 106 classes, the addition of SpFiLM layers in a UNet increased the mean Dice on the test set of 25 patients from 80.2% to 84.1%, a 4.9% relative improvement. Adding SpFiLM layers led to the best performance on both pre- and post-contrast MRI, even when controlling for network parameter counts.
Comments10 pages, 2 figures. Accepted at MLCN 2026 (MICCAI workshop)
