|Title||Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Alegro M, Theofilas P, Nguy A, Castruita PA, Seeley W, Heinsen H, Ushizima DM, Grinberg LT|
|Journal||J Neurosci Methods|
|Date Published||2017 Apr 15|
|Keywords||Alzheimer Disease, Brain, Cell Count, Fluorescent Antibody Technique, Humans, Image Processing, Computer-Assisted, Machine Learning, Microscopy, Fluorescence, Pattern Recognition, Automated, Reproducibility of Results|
BACKGROUND: Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility.
NEW METHOD: Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set.
RESULTS: Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings.
COMPARISON WITH EXISTING METHODS: We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples.
CONCLUSION: The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks.
|Alternate Journal||J. Neurosci. Methods|
|PubMed Central ID||PMC5600818|
|Grant List||TL4 GM118986 / GM / NIGMS NIH HHS / United States |
P50 AG023501 / AG / NIA NIH HHS / United States
K24 AG053435 / AG / NIA NIH HHS / United States
R01 AG040311 / AG / NIA NIH HHS / United States
P01 AG019724 / AG / NIA NIH HHS / United States