模态无关输入通道使得在训练期间缺失序列的多模态MRI脑病变分割成为可能
Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Training
AI总结：
本研究提出一种基于U-net架构的模态无关输入通道方法，通过合成人工MRI模态增强训练，使分割模型能处理训练时未见模态及异构组合，在8个数据库、5种病理和8种模态上验证了有效性。
摘要：
分割模型是检测和分析脑MRI病变的重要工具。根据所成像的脑病理类型，MRI扫描仪可以获取多种不同的图像模态（对比度）。大多数用于多模态脑MRI的分割模型局限于固定模态，无法在推理时有效处理新模态。一些模型能够泛化到未见过的模态，但可能丢失具有判别性的模态特定信息。本研究旨在开发一种模型，能够对包含训练期间未见过的图像模态、先前见过的模态以及两者的异构组合的数据进行推理，从而允许用户利用任何可用的成像模态。我们证明，通过对U-net架构进行简单且实用的修改，即集成一个模态无关的输入通道或路径，并辅以模态特定的输入通道，即可实现这一目标。为了训练这一模态无关组件，我们开发了一种图像增强方案，该方案合成人工MRI模态。增强操作差异性地改变病理和健康脑组织的外观，以在它们之间创建人工对比，同时保持真实的解剖完整性。我们使用8个MRI数据库评估了该方法，这些数据库包含5种病理类型（中风、肿瘤、创伤性脑损伤、多发性硬化症和白质高信号）和8种模态（T1、T1+对比剂、T2、PD、SWI、DWI、ADC和FLAIR）。结果表明，该方法保留了有效处理训练期间遇到的MRI模态的能力，同时能够处理新的、未见过的模态以改善其分割性能。项目代码：https://github.com/Anthony-P-Addison/AGN-MOD-SEG
英文摘要：
Segmentation models are important tools for the detection and analysis of lesions in brain MRI. Depending on the type of brain pathology that is imaged, MRI scanners can acquire multiple, different image modalities (contrasts). Most segmentation models for multimodal brain MRI are restricted to fixed modalities and cannot effectively process new ones at inference. Some models generalize to unseen modalities but may lose discriminative modality-specific information. This work aims to develop a model that can perform inference on data that contain image modalities unseen during training, previously seen modalities, and heterogeneous combinations of both, thus allowing a user to utilize any available imaging modalities. We demonstrate this is possible with a simple, thus practical alteration to the U-net architecture, by integrating a modality-agnostic input channel or pathway, alongside modality-specific input channels. To train this modality-agnostic component, we develop an image augmentation scheme that synthesizes artificial MRI modalities. Augmentations differentially alter the appearance of pathological and healthy brain tissue to create artificial contrasts between them while maintaining realistic anatomical integrity. We evaluate the method using 8 MRI databases that include 5 types of pathologies (stroke, tumours, traumatic brain injury, multiple sclerosis and white matter hyperintensities) and 8 modalities (T1, T1+contrast, T2, PD, SWI, DWI, ADC and FLAIR). The results demonstrate that the approach preserves the ability to effectively process MRI modalities encountered during training, while being able to process new, unseen modalities to improve its segmentation. Project code: https://github.com/Anthony-P-Addison/AGN-MOD-SEG
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