Table 3 Summary of performance metrics on test samples, for both SEM and TEM models.

From: AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

Modality

Model

Test sample(s)

Axon Dice similarity

Myelin Dice similarity

Pixel-wise accuracy

Sensitivity

Precision

SEM

Trained on rat samples

Rat 1

0.9089

0.8193

0.8510

0.9699

0.8468

Rat 2

0.9244

0.8389

0.8822

0.9876

0.7987

Human

0.8089

0.7629

0.8114

0.9300

0.7306

TEM

Trained on mice samples

Mice

0.9493

0.8552

0.9451

0.9597

0.9647

Macaque

0.9069

0.7519

0.8438

0.9429

0.8129

  1. The SEM model was trained on rat spinal cord samples, and evaluated on rat and human spinal cord samples, while the TEM model was trained on mice brain samples, and evaluated on mice and macaque brain samples. For each sample, axon Dice, myelin Dice, pixel-wise accuracy, sensitivity and precision were computed. Axon and myelin Dice measure the similarity between the axon/myelin segmentation masks and the ground truth. Pixel-wise accuracy is a measure of the ratio of correctly classified pixels. Sensitivity and precision values are an indication of the capability to detect true axonal fibers and to avoid segmentation of false axonal fibers. Note that for the mice, 24 samples of the same size were used: performance metrics shown are means between all samples.