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.

翻译：病理学家需要整合不同染色病理切片的信息以实现精确诊断，可形变配准是融合多模态病理切片的关键技术。本文提出了一种基于混合深度特征的染色病理样本可形变配准框架。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完成点匹配。随后，为减少误匹配，引入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，基于上述匹配点，采用插值方法生成用于病理图像配准的可形变矢量场。我们在与非刚性组织学图像配准挑战赛（ANHIR，与IEEE ISBI 2019会议共同组织）的数据集上评估了该方法。与传统方法相比，本技术的平均配准目标误差（rTRE）达到0.0034，性能提升17%。所提方法在测试数据集评估中取得最优性能，位列第一。该基于混合深度特征的配准方法有望成为可靠的病理图像配准技术。