A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images
This work addresses the need for reliable image registration in pathology to aid pathologists in diagnosis, representing a domain-specific incremental improvement.
The paper tackles the problem of accurately registering multi-modal pathology images for diagnosis by proposing a hybrid deep feature-based deformable image registration method, which outperforms traditional approaches by 17% with an Average-Average registration target error of 0.0034 and achieves state-of-the-art performance, ranking 1st on a benchmark dataset.
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.