  Recent studies have demonstrated the superiority of deep learning in medical
image analysis, especially in cell instance segmentation, a fundamental step
for many biological studies. However, the excellent performance of the neural
networks requires training on large, unbiased dataset and annotations, which is
labor-intensive and expertise-demanding. This paper presents an end-to-end
framework to automatically detect and segment NeuN stained neuronal cells on
histological images using only point annotations. Unlike traditional nuclei
segmentation with point annotation, we propose using point annotation and
binary segmentation to synthesize pixel-level annotations. The synthetic masks
are used as the ground truth to train the neural network, a U-Net-like
architecture with a state-of-the-art network, EfficientNet, as the encoder.
Validation results show the superiority of our model compared to other recent
methods. In addition, we investigated multiple post-processing schemes and
proposed an original strategy to convert the probability map into segmented
instances using ultimate erosion and dynamic reconstruction. This approach is
easy to configure and outperforms other classical post-processing techniques.
This work aims to develop a robust and efficient framework for analyzing
neurons using optical microscopic data, which can be used in preclinical
biological studies and, more specifically, in the context of neurodegenerative
diseases.
