A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images

TL;DR

This paper introduces a hybrid deep learning-based deformable image registration framework for pathology images, combining dense feature extraction, outlier detection, and interpolation to achieve state-of-the-art accuracy in aligning stained tissue slices.

Contribution

The paper presents a novel hybrid deep feature-based registration method that improves accuracy and robustness in pathology image alignment, outperforming traditional approaches.

Findings

Outperforms traditional methods by 17% in registration accuracy

Achieves a low average registration error of 0.0034

Ranks 1st in the ANHIR challenge

Abstract

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.