Figure 3
- ID
- ZDB-FIG-200523-7
- Publication
- Hailstone et al., 2020 - CytoCensus, mapping cell identity and division in tissues and organs using machine learning
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(A) Performance in identifying NBs from 3D confocal image data of a live brain labelled with Jupiter::GFP, Histone::RFP. (A′) Ground Truth manual identification of NB centres. A′′ to ′′′′) Output images comparing NB identification by Ilastik, Fiji-Weka and CytoCensus, white overlay. (Av) Plot comparing object centre detection by TrackMate spot detection, RACE, Fiji-Weka, Ilastik Pixel Classification and CytoCensus (error bars are standard deviation). CytoCensus achieves a significantly better F1-score than Ilastik (p=0.01, n = 3) and FIJI (p=0.005, n = 3). (one-way RM-ANOVA with post hoc t-tests) (B) Comparison of algorithm performance for a 3D neutral challenge data set (B′, see Materials and methods). (B′′, B′′′) Output images comparing object centre determination by Ilastik Pixel Classification and CytoCensus. Segmentation results are shown as green outlines, object centre determination is show as a cyan point. (B′′′′) Plot comparing object centre determination accuracy for the 3D neutral challenge dataset (error bars are standard deviation; p<0.001, Welch’s t-test, n = 25). Scale bars (B) 20 µm; (A′) 50 µm. |