For samples like this, there often is not a good answer since there is seldom enough information in the image to accurately segment the cells. Especially not in a single channel. At least one other group (unpublished) has created a deep learning algorithm specifically for segmenting cells in this sort of area on their own, since nothing off the shelf worked well. Specific algorithms like that tend to apply to specific staining patterns and imaging modalities, though.
DeepCell might be worth a shot if you have a second channel that consistently separates the cells (some universal cytoplasmic marker), but I have not had a great deal of luck with it.
The most success I had with lymph nodes (similar density) was taking the image on confocal at 40x or higher. Then there was enough resolution, and the depth of focus was thin enough, that segmentation became reasonable based on the information in the nuclear channel.