PseudoNeuronGAN: Unpaired synthetic image to pseudo-neuron image translation for label-free neuron instance segmentation

Zhenzhen You, Ming Jiang, Zhenghao Shi, Cheng Shi, Shuangli Du, Minghua Zhao, Anne-Sophie Hérard, Nicolas Souedet, Thierry Delzescaux

Abstract: Accurate neuron instance segmentation is of great significance in the field of neuroscience. The prerequisite for obtaining high-precision segmentation results using deep learning models is to have a large number of labeled datasets. However, in areas such as the dentate gyrus of the hippocampus where tens of thousands of neurons are aggregated, neuroscientists are unable to label neuron pixels. In this paper, we propose a pipeline for label-free neuron instance segmentation. Firstly, PseudoNeuronGAN, an unpaired synthetic image to pseudo-neuron image translation network, is proposed. Without requiring any manual labeling, synthetic cell images with known centroid labels and real neuron images are sufficient to generate a pseudo-neuron dataset. Since centroid labels are constraints to prevent neuron loss during the translation process, they are consistent in both the synthetic dataset and the generated pseudo-neuron dataset, and can be set as labels for pseudo-neuron images to train deep learning networks to predict the centroids of real neurons. Finally, based on the detected neuron centroids, neuron instance segmentation can be obtained by using competitive region growing algorithm. Experiments show that our pipeline succeeds in performing neuron instance segmentation without the need for manual annotations. PseudoNeuronGAN to generate a labeled pseudo-neuron dataset will greatly reduce the tedious labeling work by neuroscientists, and the accuracy of centroid labels is no longer biased by subjective factors. In terms of instance segmentation performance, the average F-score calculated by classical deep learning models trained on the pseudo-neuron dataset exceeds the average F-score trained on a limited number of real neuron dataset, reflecting the high quality of the generated pseudo-neuron dataset. Our critical code of PseudoNeuronGAN is available at https://github.com/zhenzhen89/PseudoNeuronGAN.

External IDs:dblp:journals/ijon/YouJSSDZHSD25