Brain slices microscopic detection using simplified SSD with cycle-GAN data augmentation

Abstract

Orderly automatic collection of brain slices on the silicon substrate is critical for understanding the working principle of the whole-brain neural network. Accurate and real-time brain slices detection with microscopic CCD is crucial for automatic collection of brain slices. To solve this task, an efficient simplified SSD detection model with Cycle-GAN data augmentation is presented in this paper. The proposed simplified SSD streamlines the detection network of the original SSD architecture, leading to a more rapid detection. Moreover, the proposed Cycle-GAN data augmentation method overcomes the limitation of training images. To verify the effectiveness of the proposed method, experiments are conducted with a self-made brain slices dataset. The experiment results suggest that, the proposed method has a good performance of rapidly detecting brain slices with only a small training dataset.