ArXiv Preprint
Background: Image analysis applications in digital pathology include various
methods for segmenting regions of interest. Their identification is one of the
most complex steps, and therefore of great interest for the study of robust
methods that do not necessarily rely on a machine learning (ML) approach.
Method: A fully automatic and optimized segmentation process for different
datasets is a prerequisite for classifying and diagnosing Indirect
ImmunoFluorescence (IIF) raw data. This study describes a deterministic
computational neuroscience approach for identifying cells and nuclei. It is far
from the conventional neural network approach, but it is equivalent to their
quantitative and qualitative performance, and it is also solid to adversative
noise. The method is robust, based on formally correct functions, and does not
suffer from tuning on specific data sets. Results: This work demonstrates the
robustness of the method against the variability of parameters, such as image
size, mode, and signal-to-noise ratio. We validated the method on two datasets
(Neuroblastoma and NucleusSegData) using images annotated by independent
medical doctors. Conclusions: The definition of deterministic and formally
correct methods, from a functional to a structural point of view, guarantees
the achievement of optimized and functionally correct results. The excellent
performance of our deterministic method (NeuronalAlg) to segment cells and
nuclei from fluorescence images was measured with quantitative indicators and
compared with those achieved by three published ML approaches.
Giuseppe Giacopelli, Michele Migliore, Domenico Tegolo
2022-12-28
