Mask-RCNN and U-net Ensembled for Nuclei Segmentation
This work addresses nuclei segmentation for biomedical imaging, but it is incremental as it combines existing methods without introducing new paradigms.
The researchers tackled nuclei segmentation by comparing U-Net and Mask-RCNN, finding they had complementary strengths, and developed an ensemble model that significantly outperformed both individual models.
Nuclei segmentation is both an important and in some ways ideal task for modern computer vision methods, e.g. convolutional neural networks. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the right model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN in the nuclei segmentation task and find that they have different strengths and failures. To get the best of both worlds, we develop an ensemble model to combine their predictions that can outperform both models by a significant margin and should be considered when aiming for best nuclei segmentation performance.