FIESTA：用于纤维束成像中精确纤维分割的自动编码器
FIESTA: Autoencoders for accurate fiber segmentation in tractography
AI总结：
本研究提出基于深度自动编码器的FIESTA流程，结合对比学习与潜在空间生成采样，提升白质束分割的覆盖范围与可靠性，性能优于现有主流方法且校准成本低。
摘要：
白质束分割是现代纤维束成像的核心基础，用于研究神经疾病、神经外科和衰老等领域的大脑结构连接性。本研究提出了FIESTA（基于自动编码器的纤维束成像纤维分割，全称为FIbEr Segmentation in Tractography using Autoencoders），这是一个可靠、稳健、完全自动化且易于半自动校准的流程，基于深度自动编码器实现，能够分割并完整填充白质束。该流程建立在先前研究的基础上，这些研究已证明自动编码器可成功应用于纤维束成像中的流线过滤、束分割和流线生成。我们提出的方法通过对受试者脑束和图谱脑束进行潜在空间播种，利用生成式采样恢复难以追踪的脑束，从而提升了脑束分割的覆盖范围。该方法结合基于自动编码器的建模与对比学习，学习流线的潜在空间。借助标准空间（MNI）中的脑束图谱，我们的方法利用每条纤维束成像流线与脑束图谱中最近邻脑束之间的自动编码器潜在距离，对新的纤维束成像数据进行分割。通过使用自动编码器生成新的流线，在保持解剖学正确性的同时增加每条脑束的空间覆盖范围，恢复难以追踪的流线，从而提升了受试者内脑束的可靠性。结果表明，我们的方法比RecoBundles、RecoBundlesX、TractSeg、White Matter Analysis和XTRACT等当前最先进的自动化虚拟分割方法更可靠。我们的框架仅需少量校准工作，即可实现从一种解剖学脑束定义到另一种的转换。总体而言，这些结果表明我们的框架提升了当前最先进脑束分割框架的实用性和易用性。
英文摘要：
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.
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