  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.
