一种用于病理图像的基于混合深度特征的可变形图像配准方法
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images
AI总结：
针对多模态染色病理切片融合需求，本文提出结合基于检测器与无检测器深度特征、孤立森林及局部仿射校正的可变形配准框架，在ANHIR数据集上以rTRE 0.0034排名第一。
摘要：
病理学家需要结合来自不同染色病理切片的信息以进行准确诊断。可变形图像配准是融合多模态病理切片的一项必要技术。本文提出了一种用于染色病理样本的、基于混合深度特征的可变形图像配准框架。我们首先通过基于检测器和无检测器的深度学习特征网络提取密集特征点，并进行点匹配。随后，为进一步减少错误匹配，提出了一种结合孤立森林统计模型与局部仿射校正模型的离群点检测方法。最后，插值方法基于上述匹配点生成用于病理图像配准的可变形向量场。我们在与 IEEE ISBI 2019 会议联合举办的非刚性组织学图像配准（ANHIR）挑战赛数据集上评估了所提方法。我们的技术较传统方法性能提升17%，平均-平均配准目标误差（rTRE）达到0.0034。所提方法取得了最先进的性能，并在测试数据集评估中排名第一。所提出的基于混合深度特征的配准方法有可能成为病理图像配准的可靠方法。
英文摘要：
Pathologists need to combine information from differently stained pathology slices for accurate diagnosis. Deformable image registration is a necessary technique for fusing multi-modal pathology slices. This paper proposes a hybrid deep feature-based deformable image registration framework for stained pathology samples. We first extract dense feature points via the detector-based and detector-free deep learning feature networks and perform points matching. Then, to further reduce false matches, an outlier detection method combining the isolation forest statistical model and the local affine correction model is proposed. Finally, the interpolation method generates the deformable vector field for pathology image registration based on the above matching points. We evaluate our method on the dataset of the Non-rigid Histology Image Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019 conference. Our technique outperforms the traditional approaches by 17% with the Average-Average registration target error (rTRE) reaching 0.0034. The proposed method achieved state-of-the-art performance and ranked 1st in evaluating the test dataset. The proposed hybrid deep feature-based registration method can potentially become a reliable method for pathology image registration.
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