NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation
This addresses data imbalance issues in pathology image analysis for researchers and practitioners, though it is incremental as it builds on existing augmentation and diffusion model techniques.
The study tackled data imbalance in nuclei instance segmentation by introducing NucleiMix, a data augmentation method that increases rare-type nuclei in datasets, resulting in enhanced segmentation and classification quality as demonstrated on three public datasets with two models.
Nuclei instance segmentation is an essential task in pathology image analysis, serving as the foundation for many downstream applications. The release of several public datasets has significantly advanced research in this area, yet many existing methods struggle with data imbalance issues. To address this challenge, this study introduces a data augmentation method, called NucleiMix, which is designed to balance the distribution of nuclei types by increasing the number of rare-type nuclei within datasets. NucleiMix operates in two phases. In the first phase, it identifies candidate locations similar to the surroundings of rare-type nuclei and inserts rare-type nuclei into the candidate locations. In the second phase, it employs a progressive inpainting strategy using a pre-trained diffusion model to seamlessly integrate rare-type nuclei into their new environments in replacement of major-type nuclei or background locations. We systematically evaluate the effectiveness of NucleiMix on three public datasets using two popular nuclei instance segmentation models. The results demonstrate the superior ability of NucleiMix to synthesize realistic rare-type nuclei and to enhance the quality of nuclei segmentation and classification in an accurate and robust manner.