Evaluation of automated segmentation algorithms for neurons in macaque cerebral microscopic images.
macaque brain
microscopy images
neuron segmentation
segmentation evaluation
Journal
Microscopy research and technique
ISSN: 1097-0029
Titre abrégé: Microsc Res Tech
Pays: United States
ID NLM: 9203012
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
revised:
21
03
2021
received:
23
01
2021
accepted:
07
04
2021
pubmed:
29
4
2021
medline:
16
9
2021
entrez:
28
4
2021
Statut:
ppublish
Résumé
Accurate cerebral neuron segmentation is required before neuron counting and neuron morphological analysis. Numerous algorithms for neuron segmentation have been published, but they are mainly evaluated using limited subsets from a specific anatomical region, targeting neurons of clear contrast and/or neurons with similar staining intensity. It is thus unclear how these algorithms perform on cerebral neurons in diverse anatomical regions. In this article, we introduce and reliably evaluate existing machine learning algorithms using a data set of microscopy images of macaque brain. This data set highlights various anatomical regions (e.g., cortex, caudate, thalamus, claustrum, putamen, hippocampus, subiculum, lateral geniculate, globus pallidus, etc.), poor contrast, and staining intensity differences of neurons. The evaluation was performed using 10 architectures of six classic machine learning algorithms in terms of typical Recall, Precision, F-score, aggregated Jaccard index (AJI), as well as a performance ranking of algorithms. F-score of most of the algorithms is superior to 0.7. Deep learning algorithms facilitate generally higher F-scores. U-net with suitable layer depth has been evaluated to be excellent classifiers with F-score of 0.846 and 0.837 when performing cross validation. The evaluation and analysis indicate the performance gap among algorithms in various anatomical regions and the strengths and limitations of each algorithm. The comparative result highlights at the same time the importance and difficulty of neuron segmentation and provides clues for future improvement. To the best of our knowledge, this work is the first comprehensive study for neuron segmentation in such large-scale anatomical regions. Neuron segmentation plays a critical role in extracting cerebral information, such as neuron counting and neuron morphological analysis. Accurate automated cerebral neuron segmentation is a challenging task due to different kinds, poor contrast, staining intensity differences, and fuzzy boundaries of neurons. The comprehensive evaluation and analysis of performance among existing machine learning algorithms in diverse anatomical regions allows to make clear of the strengths and limitations of state-of-the-art algorithm. The comprehensive study provides clues for future improvement and creation of automated methods.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2311-2324Subventions
Organisme : French national funds (PIA2' program)
ID : P112331-3422142
Organisme : Fund of Doctoral Start-up of Xi'an University of Technology
ID : 112/256081811
Organisme : General Program of National Natural Science Foundation of China
ID : 62076198
Organisme : Key Program of Natural Science Foundation of Shaanxi Province of China
ID : 2020GXLH-Y005
Organisme : National Nature Science Foundation of China
ID : 61901363
Organisme : National Nature Science Foundation of China
ID : 61902313
Organisme : Natural Science Foundation of Shaanxi Province
ID : 2020JM-463
Organisme : Natural Science Foundation of Shaanxi Province
ID : 2020JQ-648
Organisme : Natural Science Foundation of Shaanxi Province
ID : 2020JQ-652
Organisme : Natural Science Foundation of Shaanxi Provincial Department of Education
ID : 20JK0795
Informations de copyright
© 2021 Wiley Periodicals LLC.
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