This workshop is partially supported
by Australian Research Council,
discovery project DP140102794 &
ARC Future Fellowship (FT110100623).
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Summary of the results

After the first call for participation, six teams registered for participation in the challenge, but only two teams successfully submitted working systems, where both methods present competitive results. As a baseline, we also present our latest method to this challenge. This page shows the quantitative evaluation of all methods.

Evaluation method

  • Dataset: 810 synthetic cell images from test dataset, no ground truth was given previously.

  • Good Segmentation: a “good” segmentation is considered to be one with Dice > 0.7 .

  • Abbreviations:

    • FNo: object-based False negative rate

    • TPp: pixel-based True positive rate

    • FPp: pixel-based False positive rate

Quantitative Comparison

The quantitative comparison contains the evaluation of cytoplasm segmentation and nuclei detection.

Table I - Nuclei

Precision (Object) Recall (Object) Precision (Pixel) Recall (Pixel) ZSI (Pixel)
Dani 0.959 0.895 0.968±0.055 0.871±0.069 0.914±0.039
Masoud 0.903 0.893 0.901±0.097 0.916±0.093 0.900±0.053
Lu 0.977 0.883 0.942±0.078 0.912±0.081 0.921±0.049

Table II - Cytoplasm

Dice FNo TPp FPp
Ushizima 0.872±0.082 0.267±0.278 0.841±0.130 0.002±0.003
Nosrati 0.871±0.075 0.111±0.166 0.875±0.086 0.004±0.004
Lu 0.893±0.082 0.316±0.295 0.905±0.096 0.004±0.005


  1. SEGMENTATION OF SUBCELLULAR COMPARTIMENTS COMBINING SUPERPIXEL REPRESENTATION WITH VORONOI DIAGRAMS, Daniela M. Ushizima (Lawrence Berkeley National Laboratory, Berkeley, CA, USA), Andrea G. C. Bianchi and Claudia M. Carneiro (Federal University of Ouro Preto, Ouro Preto, MG, Brazil) [PDF]

  2. A VARIATIONAL APPROACH FOR OVERLAPPING CELL SEGMENTATION, Masoud S. Nosrati and Ghassan Hamarneh (Medical Image Analysis Lab., Simon Fraser University, BC, Canada) [PDF]