White Matter Tract Segmentation is imperative for studying brain structural connectivity, neurological disorders and neurosurgery. This task remains complex, as tracts differ among themselves, across subjects and conditions, yet have similar 3D structure across hemispheres and subjects. To address these challenges, we propose TrackletGPT, a language-like GPT framework which reintroduces sequential information in tokens using tracklets. TrackletGPT generalises seamlessly across datasets, is fully automatic, and encodes granular sub-streamline segments, Tracklets, scaling and refining GPT models in Tractography Segmentation. Based on our experiments, TrackletGPT outperforms state-of-the-art methods on average DICE, Overlap and Overreach scores on TractoInferno and HCP datasets, even on inter-dataset experiments.

翻译：白质束分割对于研究大脑结构连接性、神经系统疾病及神经外科手术至关重要。该任务仍然十分复杂，因为白质束在不同个体、不同条件之间存在差异，但在大脑半球和个体间却具有相似的三维结构。为应对这些挑战，我们提出了TrackletGPT，一种类语言GPT框架，该框架通过使用轨迹片段（tracklets）在标记中重新引入序列信息。TrackletGPT能够无缝泛化至不同数据集，实现全自动处理，并对细粒度的亚流线分段——轨迹片段进行编码，从而在纤维束成像分割中扩展并优化了GPT模型。根据我们的实验，在TractoInferno和HCP数据集上，TrackletGPT在平均DICE、重叠度和过度延伸分数上均优于现有最先进方法，即使在跨数据集实验中亦是如此。