TY - JOUR
T1 - Neuron cell count with deep learning in highly dense hippocampus images
AU - Vizcaíno, Alfonso
AU - Sánchez-Cruz, Hermilo
AU - Sossa, Humberto
AU - Quintanar, J. Luis
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Neural cell counting is one of the ways in which damage caused by neurodegenerative diseases can be assessed, but it is not an easy task when it comes to neuronal counting in the most densely populated areas of the hippocampus. In this regard, this work presents a leveraged deep learning (DL) model, an innovative way to treat histological images and their correspondent ground truth information, where highly dense cell population with fuzzy cell boundaries and low image quality exist. The proposed model achieves state-of-the-art results in the neuron cell count problem for the highly dense area of DG and CA hippocampus regions, by making use of better pixel characterization which in turn also delivers a more efficient model size and reduces training time. Furthermore, we show that the proposed image treatment can be applied to other DL models and help them to obtain a 12% performance increase. Also, we demonstrate that with the proposed methodology, an innovative and reliable way to count neural cells with poor image condition in histological analysis has been carried out.
AB - Neural cell counting is one of the ways in which damage caused by neurodegenerative diseases can be assessed, but it is not an easy task when it comes to neuronal counting in the most densely populated areas of the hippocampus. In this regard, this work presents a leveraged deep learning (DL) model, an innovative way to treat histological images and their correspondent ground truth information, where highly dense cell population with fuzzy cell boundaries and low image quality exist. The proposed model achieves state-of-the-art results in the neuron cell count problem for the highly dense area of DG and CA hippocampus regions, by making use of better pixel characterization which in turn also delivers a more efficient model size and reduces training time. Furthermore, we show that the proposed image treatment can be applied to other DL models and help them to obtain a 12% performance increase. Also, we demonstrate that with the proposed methodology, an innovative and reliable way to count neural cells with poor image condition in histological analysis has been carried out.
KW - Deep learning
KW - Hematoxylin Eosin
KW - Histological images
KW - Neuron cell count
UR - http://www.scopus.com/inward/record.url?scp=85134561296&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2022.118090
DO - 10.1016/j.eswa.2022.118090
M3 - Artículo
AN - SCOPUS:85134561296
SN - 0957-4174
VL - 208
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 118090
ER -