GAN based Unsupervised Segmentation: Should We Match the Exact Number of Objects

Abstract

The unsupervised segmentation is an increasingly popular topic in biomedical image analysis. The basic idea is to approach the supervised segmentation task as an unsupervised synthesis problem, where the intensity images can be transferred to the annotation domain using cycle-consistent adversarial learning. The previous studies have shown that the macro-level (global distribution level) matching on the number of the objects (e.g., cells, tissues, protrusions etc.) between two domains resulted in better segmentation performance. However, no prior studies have exploited whether the unsupervised segmentation performance would be further improved when matching the exact number of objects at micro-level (mini-batch level). In this paper, we propose a deep learning based unsupervised segmentation method for segmenting highly overlapped and dynamic sub-cellular microvilli. With this challenging task, both micro-level and macro-level matching strategies were evaluated. To match the number of objects at the micro-level, the novel fluorescence-based micro-level matching approach was presented. From the experimental results, the micro-level matching did not improve the segmentation performance, compared with the simpler macro-level matching.

ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations

“…Therefore, with knowledge of shapes associated with microvilli (stick-shaped) and HeLa cell images (ball-shaped), we randomly generate fake annotations with repetitive sticks and circles to model the shape of microvilli and HeLa cells, respectively. The network structure, training process and parameters follows [38].…”