使用深度网络的可解释无监督多模态图像配准
Explainable unsupervised multi-modal image registration using deep networks
AI总结：
本研究提出一种基于深度网络的无监督多模态图像配准方法，通过集成Grad-CAM可解释性框架，使模型在保持优于Syn方法性能的同时实现完全可解释，为推广至更多医学成像数据奠定基础。
摘要：
基于磁共振成像（MRI）的临床决策结合了来自多个MRI序列（定义为“模态”）的互补信息。MRI图像配准旨在从不同模态、时间点和切片几何地“配对”诊断。模态内和模态间MRI配准都是临床MRI设置中的关键组件。此外，能够同时处理仿射和非刚性配准的MRI图像处理流程至关重要，因为真实MRI数据场景中可能同时发生这两种变形。与图像分类不同，可解释性在图像配准深度学习（DL）方法中并未得到普遍解决，因为解释模型-数据行为与变换场之间的关系具有挑战性。为了妥善解决这一问题，我们在无监督多模态和多器官图像配准DL方法的每个主要组件中集成了基于Grad-CAM的可解释性框架。我们之前证明，我们的方法能够达到优于当前标准Syn方法的性能。在本研究中，我们展示了我们的DL模型变得完全可解释，为将该框架推广到更多医学成像数据奠定了基础。
英文摘要：
Clinical decision making from magnetic resonance imaging (MRI) combines complementary information from multiple MRI sequences (defined as 'modalities'). MRI image registration aims to geometrically 'pair' diagnoses from different modalities, time points and slices. Both intra- and inter-modality MRI registration are essential components in clinical MRI settings. Further, an MRI image processing pipeline that can address both afine and non-rigid registration is critical, as both types of deformations may be occuring in real MRI data scenarios. Unlike image classification, explainability is not commonly addressed in image registration deep learning (DL) methods, as it is challenging to interpet model-data behaviours against transformation fields. To properly address this, we incorporate Grad-CAM-based explainability frameworks in each major component of our unsupervised multi-modal and multi-organ image registration DL methodology. We previously demonstrated that we were able to reach superior performance (against the current standard Syn method). In this work, we show that our DL model becomes fully explainable, setting the framework to generalise our approach on further medical imaging data.
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