  White matter bundle segmentation is a cornerstone of modern tractography to
study the brain's structural connectivity in domains such as neurological
disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr
Segmentation in Tractography using Autoencoders), a reliable and robust, fully
automated, and easily semi-automatically calibrated pipeline based on deep
autoencoders that can dissect and fully populate white matter bundles. This
pipeline is built upon previous works that demonstrated how autoencoders can be
used successfully for streamline filtering, bundle segmentation, and streamline
generation in tractography. Our proposed method improves bundle segmentation
coverage by recovering hard-to-track bundles with generative sampling through
the latent space seeding of the subject bundle and the atlas bundle. A latent
space of streamlines is learned using autoencoder-based modeling combined with
contrastive learning. Using an atlas of bundles in standard space (MNI), our
proposed method segments new tractograms using the autoencoder latent distance
between each tractogram streamline and its closest neighbor bundle in the atlas
of bundles. Intra-subject bundle reliability is improved by recovering
hard-to-track streamlines, using the autoencoder to generate new streamlines
that increase the spatial coverage of each bundle while remaining anatomically
correct. Results show that our method is more reliable than state-of-the-art
automated virtual dissection methods such as RecoBundles, RecoBundlesX,
TractSeg, White Matter Analysis and XTRACT. Our framework allows for the
transition from one anatomical bundle definition to another with marginal
calibration efforts. Overall, these results show that our framework improves
the practicality and usability of current state-of-the-art bundle segmentation
framework.
