Publication
CellSpot: Deep Learning-Based Efficient Cell Center Detection in Microscopic Images
Nabeel Khalid; Maria Caropresse; Gillian Lovell; Andreas Dengel; Sheraz Ahmed
In: 33rd International Conference on Artificial Neural Networks ICANN. International Conference on Artificial Neural Networks (ICANN-2024), September 17-20, Lugano, Switzerland, Springer Nature, Switzerland, 9/2024.
Abstract
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 resourceeffective
analysis in biological research and medical diagnostics.