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Updated: Apr 9, 2026
Author Spotlight: Decellularization-Based Quantification of Skeletal Muscle Fatty Infiltration
Published on: June 9, 2023
Fast and accurate identification of fat droplets in histological images
André Homeyer1, Andrea Schenk1, Janine Arlt2
1Fraunhofer MEVIS, Universitätsallee 29, 28359 Bremen, Germany.
Background And Objective:
The accurate identification of fat droplets is a prerequisite for the automatic quantification of steatosis in histological images. A major challenge in this regard is the distinction between clustered fat droplets and vessels or tissue cracks.
Methods:
We present a new method for the identification of fat droplets that utilizes adjacency statistics as shape features. Adjacency statistics are simple statistics on neighbor pixels.
Results:
The method accurately identified fat droplets with sensitivity and specificity values above 90%. Compared with commonly-used shape features, adjacency statistics greatly improved the sensitivity toward clustered fat droplets by 29% and the specificity by 17%. On a standard personal computer, megapixel images were processed in less than 0.05s.
Conclusions:
The presented method is simple to implement and can provide the basis for the fast and accurate quantification of steatosis.
