Nucleus Detection Based on Adversarial Domain Adaptation with Cross-Domain Consistency

Automatic cell/nucleus detection is a prerequisite for various quantitative analyses on microscopy image. However, previous deep learning methods require enough annotated microscopy images for better performance, which is a great challenge for microscopy image due to limited annotation and high cost. This paper proposes an end-to-end adversarial learning model with unsupervised domain adaptation for cell/nucleus detection. Different staining microscopy images transformation and cell/nucleus detection are merged into one end-to-end model to achieve mutual restriction of accuracy. Furthermore, a cross-domain consistency loss is introduced, which can refine the results of image transformation and localize cells synchronously. The experiments conclude that proposed method achieves the best F1 scores compared with other methods on cell/nucleus detection of different staining microscopy images. Moreover, ablation study also approves the effectiveness of cross-domain consistency loss.