  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.
