Table 5 Training protocol for the Ex vivo high-resolution spinal cord dataset.

Cropping

All the slices were center-cropped to a 200 × 200 pixels size.

Normalization

We performed only mean centering and standard deviation normalization of the volume intensities.

Train/validation split

For the training set we selected only 15 evenly spaced axial slices out of 4676 total slices from the volume. For the validation set, we selected 7 (evenly spaced) axial slices and our test set was comprised of 8 axial slices (also evenly distributed across the entire volume).

Batch size

We used a small batch size of only 11 samples.

Optimization

We used Adam⁴⁸ optimizer with a small learning rate \(\eta =0.001\).

Batch Normalization

We used a momentum \(\varphi =0.1\) for BatchNorm due to the small batch size.

Dropout

We used a dropout rate of 0.4.

Learning Rate Scheduling

Similar to the work²¹, we used the “poly” learning rate policy where the learning rate is defined by \(\eta ={\eta }_{{t}_{0}}\ast {(1-\frac{n}{N})}^{p}\) where \({\eta }_{{t}_{0}}\) is the initial learning rate, N is the number of epochs, n the current epoch and p the power with \(p=0.9\).

Iterations

We trained the model for 600 epochs (w/ 32 batches at each epoch).

Data augmentation

For this dataset, we used the following aforementioned augmentations: rotation, shift, scaling, channel shift, flipping and elastic deformation⁴⁰. We didn’t employed random search to avoid overfitting due to the dataset size. More details about the parameters of the data augmentation are presented in Table 6.