CP-Dilatation: A Copy-and-Paste Augmentation Method for Preserving the Boundary Context Information of Histopathology Images

TL;DR

CP-Dilatation enhances histopathology image segmentation by augmenting data with boundary-preserving dilation, improving the training process and diagnosis accuracy in medical AI applications.

Contribution

It introduces a novel augmentation method that combines copy-paste with dilation to retain boundary context in histopathology images.

Findings

Outperforms state-of-the-art augmentation baselines.

Improves segmentation accuracy on benchmark datasets.

Preserves boundary information effectively.

Abstract

Medical AI diagnosis including histopathology segmentation has derived benefits from the recent development of deep learning technology. However, deep learning itself requires a large amount of training data and the medical image segmentation masking, in particular, requires an extremely high cost due to the shortage of medical specialists. To mitigate this issue, we propose a new data augmentation method built upon the conventional Copy and Paste (CP) augmentation technique, called CP-Dilatation, and apply it to histopathology image segmentation. To the well-known traditional CP technique, the proposed method adds a dilation operation that can preserve the boundary context information of the malignancy, which is important in histopathological image diagnosis, as the boundary between the malignancy and its margin is mostly unclear and a significant context exists in the margin. In our experiments using histopathology benchmark datasets, the proposed method was found superior to the other state-of-the-art baselines chosen for comparison.