• Streamline classification usually requires registration of subject data to a common space.
  • Contribution: The main goal of this work is to provide a rotationally invariant approach for streamline segmentation based on a set of reference streamlines.

Main idea

  • Learn the probability of two streamlines to belong to the same bundle.
  • Recognise a streamline by assigning the label of the fiber with the highest similarity in a given set of reference fibers.

Rotation invariance

  • Assumption: Similarity between fibers is learned. This is supposed to generalise to

    a. misaligned

    b. rotated


  • Therefore, applying data augmentation to the reference set is sufficient for achieving rotation invariance. (I.e. augmentation independent of the training stage.)


  • FS2Net:

    • stacked LSTMs to arrive at a 32-entry feature vector
    • “L1-distance” between feature vectors

      Comment: Probably, the authors mean the difference vector.

    • classification network: ‘similar’ vs ‘dissimilar’


  • Comparison to two of their own works: ANN and BrainSegNet

    • Comment: Doubtful if this can be considered as ‘state of the art’.
  • Training:

    • BS: 11, 1000 batches per epoch -> only 11K training samples. # epochs: not clear.


  • macro classification - Gray matter fibres vs White matter fibres

    • probably: U-shaped or short fibres vs long range fibres (?)

  • micro classification - Labelling WM fibres into 8 subclasses

  • Recall - fraction of correct WM labels in macro classification


  • 3 subjects, 250K streamlines each
  • preprocessing: keep only 75% of streamline points with the highest curvature (per fibre)
  • each streamline: max of 100 points (zero padded)

Registered data

  • FS2Net achieves top performance compared to ANN and BrainSegNet in all experiments.
  • FS2Net shows better recall hinting at a better robustness towards data imbalance of GM and WM classes (macro).

Unregistered data

  • FS2Net is quite robust to rotations of testing streamlines.


  • Very nice idea. However, for the real value of their approach for streamline segmentation the evaluation is of limited power.