CellSpot: Deep Learning-Based Efficient Cell Center Detection in Microscopic Images

Cells play a fundamental role in sustaining life by performing numerous functions crucial for the survival of living organisms. The detection of cells holds paramount importance in the validation and analysis of biological hypotheses, as it offers valuable insights into the behavior, function, diagnosis, and treatment of diseases. By accurately detecting and studying cells, researchers can unravel the complexities of cellular processes, leading to advancements in understanding diseases and the development of effective therapeutic interventions. In the domain of microscopic image analysis, substantial efforts have been devoted to the quantification of cells through segmentation masks and bounding boxes. However, these methods are time-consuming and resource-intensive. To tackle this challenge, we’ve introduced a novel approach focused on cell detection using solely their centerpoints. The proposed pipeline drastically cuts down on annotation efforts while still delivering commendable performance. By leveraging the proposed method, we aim to enhance efficiency in cell detection, paving the way for more expedient and resource-effective analysis in biological research and medical diagnostics.