用空间调制技术让脑分割模型同时准确处理有无造影剂的MRI。
Spatial Feature-wise Linear Modulation (SpFiLM) for Contrast Agent-Aware Brain Parcellation
- 引入空间可变的特征调制层，根据局部脑区差异动态调整网络响应。
- 在25例测试数据上，平均Dice系数从80.2%提升至84.1%，相对提高4.9%。
- 特别适合需兼顾造影前后MRI的临床脑分割任务，如肿瘤研究。
大多数自动化脑分区工具基于T1加权（T1w）MRI开发和验证，但部分临床流程仅使用增强型T1w（T1ce）MRI，此时传统T1w训练模型性能下降。本文提出统一网络，可可靠分割含与不含造影剂的T1w MRI，通过结合两类数据并引入空间条件调制。标准特征线性调制（FiLM）对每通道应用全局缩放与偏移，但造影前后图像变化在脑区间呈局部差异，使其不适用。为此，本文提出空间特征线性调制（SpFiLM），从图像导出的空间模式生成体素级缩放与偏移。基于134名患者配对的T1w与T1ce MRI数据（106类分区），在UNet中加入SpFiLM层后，测试集25例患者的平均Dice系数从80.2%提升至84.1%，相对提升4.9%。即使控制参数量，该方法在预/后造影图像上均取得最佳性能。
原文摘要 · Abstract (English)
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.
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