  This paper investigates the application of advanced image segmentation
techniques to analyze C-fos immediate early gene expression, a crucial marker
for neural activity. Due to the complexity and high variability of neural
circuits, accurate segmentation of C-fos images is paramount for the
development of new insights into neural function. Amidst this backdrop, this
research aims to improve accuracy and minimize manual intervention in C-fos
image segmentation by leveraging the capabilities of CNNs and the Unet model.
We describe the development of a novel workflow for the segmentation process
involving Convolutional Neural Networks (CNNs) and the Unet model,
demonstrating their efficiency in various image segmentation tasks. Our
workflow incorporates pre-processing steps such as cropping, image feature
extraction, and clustering for the training dataset selection. We used an
AutoEncoder model to extract features and implement constrained clustering to
identify similarities and differences in image types. Additionally, we utilized
manual and automatic labeling approaches to enhance the performance of our
model. We demonstrated the effectiveness of our method in distinguishing areas
with significant C-fos expression from normal tissue areas. Lastly, we
implemented a modified Unet network for the detection of C-fos expressions.
This research contributes to the development of more efficient and automated
image segmentation methods, advancing the understanding of neural function in
neuroscience research.
