Mask-RCNN and U-Net Ensembled for Nuclei Segmentation
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)(2019)
摘要
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.
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关键词
nuclei segmentation,microscopy image analysis,convolutional neural networks
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