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This paper investigates the application of advanced image segmentationtechniques to analyze C-fos immediate early gene expression, a crucial markerfor neural activity. Due to the complexity and high variability of neuralcircuits, accurate segmentation of C-fos images is paramount for thedevelopment of new insights into neural function. Amidst this backdrop, thisresearch aims to improve accuracy and minimize manual intervention in C-fosimage segmentation by leveraging the capabilities of CNNs and the Unet model.We describe the development of a novel workflow for the segmentation processinvolving Convolutional Neural Networks (CNNs) and the Unet model,demonstrating their efficiency in various image segmentation tasks. Ourworkflow incorporates pre-processing steps such as cropping, image featureextraction, and clustering for the training dataset selection. We used anAutoEncoder model to extract features and implement constrained clustering toidentify similarities and differences in image types. Additionally, we utilizedmanual and automatic labeling approaches to enhance the performance of ourmodel. We demonstrated the effectiveness of our method in distinguishing areaswith significant C-fos expression from normal tissue areas. Lastly, weimplemented a modified Unet network for the detection of C-fos expressions.This research contributes to the development of more efficient and automatedimage segmentation methods, advancing the understanding of neural function inneuroscience research.

Advanced Image Segmentation Techniques for Neural Activity Detection via C-fos Immediate Early Gene Expression