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.

翻译：白质纤维束分割是现代纤维束示踪成像研究大脑结构连接性的基石，广泛应用于神经系统疾病、神经外科和衰老等领域。本研究提出FIESTA（基于自编码器的纤维束示踪成像纤维分割）框架——一种基于深度自编码器的可靠、稳健、全自动且易于半自动校准的流程，可对白质纤维束进行解剖分割并完整填充。该流程基于先前研究，即证明了自编码器可成功用于纤维束示踪成像中的流线过滤、束分割及流线生成。我们提出的方法通过受试者纤维束与图谱纤维束的潜在空间播种生成采样，恢复难以追踪的纤维束，从而提升束分割覆盖率。通过结合对比学习的自编码器模型，学习流线的潜在空间。利用标准空间（MNI）中的纤维束图谱，本文方法根据每条示踪图流线与其在纤维束图谱中最邻近束之间的自编码器潜在距离，对新的示踪图进行分割。通过生成解剖学正确且增加各纤维束空间覆盖的新流线以恢复难以追踪的流线，从而提高受试者内纤维束的可靠性。结果表明，该方法比RecoBundles、RecoBundlesX、TractSeg、白质分析和XTRACT等现有自动化虚拟解剖方法更可靠。本框架只需付出微小的校准努力即可实现不同解剖纤维束定义的转换。总体而言，这些结果表明我们的框架提升了当前最优纤维束分割框架的实用性与可用性。