BSAFusion：一种面向未配准医学图像融合的双向逐步特征对齐网络
BSAFusion: A Bidirectional Stepwise Feature Alignment Network for Unaligned Medical Image Fusion
AI总结：
针对未配准医学图像融合中特征对齐与融合需求不兼容的问题，提出BSAFusion方法，结合无模态差异特征表示与双向逐步对齐策略，实现单阶段配准融合，在多数据集上验证了有效性。
摘要：
若能在统一处理框架内通过单阶段方法同时完成未配准多模态医学图像的配准与融合，不仅可实现双任务的相互促进，还有助于降低模型复杂度。然而，该模型的设计面临特征融合与特征配准需求不兼容的挑战：具体而言，特征配准要求对应特征之间具备一致性，而特征融合则要求各特征之间具有互补性。\n为解决这一挑战，本文提出了一种名为双向逐步特征对齐与融合（Bidirectional Stepwise Feature Alignment and Fusion, BSFA-F）策略的未配准医学图像融合方法。为降低模态差异对跨模态特征匹配的负面影响，我们将无模态差异特征表示（Modal Discrepancy-Free Feature Representation, MDF-FR）方法融入BSFA-F。MDF-FR利用模态特征表示头（Modality Feature Representation Head, MFRH）整合输入图像的全局信息，通过将当前图像的MFRH所包含的信息注入到其他模态图像中，在保留不同图像携带的互补信息的同时，有效降低了模态差异对特征对齐的影响。\n在特征对齐方面，BSFA-F基于两点间矢量位移的路径无关性，采用双向逐步对齐形变场预测策略，解决了单步对齐中跨度大、形变场预测不准确的问题。最后，多模态特征融合模块实现了对齐后特征的融合。在多个数据集上的实验结果验证了所提方法的有效性。源代码可在https://github.com/slrl123/BSAFusion 获取。
英文摘要：
If unaligned multimodal medical images can be simultaneously aligned and fused using a single-stage approach within a unified processing framework, it will not only achieve mutual promotion of dual tasks but also help reduce the complexity of the model. However, the design of this model faces the challenge of incompatible requirements for feature fusion and alignment; specifically, feature alignment requires consistency among corresponding features, whereas feature fusion requires the features to be complementary to each other. To address this challenge, this paper proposes an unaligned medical image fusion method called Bidirectional Stepwise Feature Alignment and Fusion (BSFA-F) strategy. To reduce the negative impact of modality differences on cross-modal feature matching, we incorporate the Modal Discrepancy-Free Feature Representation (MDF-FR) method into BSFA-F. MDF-FR utilizes a Modality Feature Representation Head (MFRH) to integrate the global information of the input image. By injecting the information contained in MFRH of the current image into other modality images, it effectively reduces the impact of modality differences on feature alignment while preserving the complementary information carried by different images. In terms of feature alignment, BSFA-F employs a bidirectional stepwise alignment deformation field prediction strategy based on the path independence of vector displacement between two points. This strategy solves the problem of large spans and inaccurate deformation field prediction in single-step alignment. Finally, Multi-Modal Feature Fusion block achieves the fusion of aligned features. The experimental results across multiple datasets demonstrate the effectiveness of our method. The source code is available at https://github.com/slrl123/BSAFusion.
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