Microscopy cell counting with fully convolutional regression networks

Conference item

This paper concerns automated cell counting in microscopy images. The approach we take is to adapt Convolutional Neural Networks (CNNs) to regress a cell spatial density map across the image. This is applicable to situations where traditional single-cell segmentation based methods do not work well due to cell clumping or overlap. We make the following contributions: (i) we develop and compare architectures for two Fully Convolutional Regression Networks (FCRNs) for this task; (ii) since the networks are fully convolutional, they can predict a density map for an input image of arbitrary size, and we exploit this to improve efficiency at training time by training end-to-end on image patches; and (iii) we show that FCRNs trained entirely on synthetic data are able to give excellent predictions on real microscopy images without fine-tuning, and that the performance can be further improved by fine-tuning on the real images. We set a new state-of-the-art performance for cell counting on the standard synthetic image benchmarks and, as a side benefit, show the potential of the FCRNs for providing cell detections for overlapping cells.

Authors: Xie, W; Noble, J; Zisserman, A

• Publication status: Not published
• Peer review status: Peer reviewed
• Publisher: TUM Technische Universität München
• Host title: 18th International Conference on Medical Image Computing and Computer Analysis Interventions (MICCAI 2015)
• Publication date: 2015-01-01
• Acceptance date: 2015-02-02