Table 3 Quantitative performance evaluation of the cell segmentation IDL module on cell tracking challenge test Datasets39.

From: PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

CTC metrics

Cell tracking challenge testing datasets

PhC-C2DH-U373

DIC-C2DH-HeLa

Fluo-N2DL-HeLa

Fluo-N2DH-GOWT1

Fluo-N2DH-SIM+

\(OP_{CSB}\)(%)

95 (CALT-US:96.1)

84.3 (CALT-US:92.6)

86.4 (BFR-GE:95.7)

88.1 (KTH-SE:95.2)

84.3 (KIT-GE:90.5)

SEG(%)

91.7 (CALT-US:93.1)

80.1 (CALT-US:88.7)

78.8 (MU-US:92.3)

85.2 (CSU-CN:93.8)

72.2 (DKFZ-GE:83.2)

DET(%)

98.3 (CALT-US:99.0)

88.5 (CALT-US:97.5)

94.1 (KIT-GE:99.4)

91.1 (TUG-AT:98.0)

96.5 (FR-GE:98.1)

  1. This evaluation uses results computed by the challenge organizers39 upon submission of our test set results (where no ground truth was provided). The AttUnet(XAI) performance was assessed using the challenge metrics against the test ground truth masks. The \(OP_{CSB}\) metric is defined as \(0.5 \times (DET + SEG)\). Here, \(DET = 1 - \frac{\min (AOGM-D, AOGM-D_{0})}{AOGM-D_{0}}\), which represents the normalized Acyclic Oriented Graph Matching (AOGM-D) measure for detection40. The SEG measure employs the Jaccard similarity index, expressed as \(J(S, R) = \frac{|R \cap S|}{|R \cup S|}\), where R is the set of pixels in a reference object and S is the set of pixels in its matching segmented object. A reference object R and a segmented object S are considered matching if \(|R \cap S| > 0.5 \times |R|\). The top-performing apporach per dataset and per metric are mentioned as follow (approach : score) from CTC website39.
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