Accurately identifying white matter tracts in medical images is essential for various applications, including surgery planning and tract-specific analysis. Supervised machine learning models have reached state-of-the-art solving this task automatically. However, these models are primarily trained on healthy subjects and struggle with strong anatomical aberrations, e.g. caused by brain tumors. This limitation makes them unsuitable for tasks such as preoperative planning, wherefore time-consuming and challenging manual delineation of the target tract is typically employed. We propose semi-automatic entropy-based active learning for quick and intuitive segmentation of white matter tracts from whole-brain tractography consisting of millions of streamlines. The method is evaluated on 21 openly available healthy subjects from the Human Connectome Project and an internal dataset of ten neurosurgical cases. With only a few annotations, the proposed approach enables segmenting tracts on tumor cases comparable to healthy subjects (dice=0.71), while the performance of automatic methods, like TractSeg dropped substantially (dice=0.34) in comparison to healthy subjects. The method is implemented as a prototype named atTRACTive in the freely available software MITK Diffusion. Manual experiments on tumor data showed higher efficiency due to lower segmentation times compared to traditional ROI-based segmentation.

翻译：准确识别医学图像中的白质纤维束对于手术计划及纤维束特异性分析等多种应用至关重要。监督式机器学习模型在自动完成此任务方面已达到最优水平。然而，这些模型主要基于健康受试者训练，在处理由脑肿瘤等引起的严重解剖结构异常时表现不佳。这一局限性使其不适用于术前规划等任务，因此通常需要耗时且具有挑战性的手动目标纤维束勾画。我们提出了一种基于熵的半自动化主动学习方法，用于从包含数百万条流线的全脑纤维束成像中快速且直观地分割白质纤维束。该方法在人类连接组计划的21名公开健康受试者及一个包含10例神经外科病例的内部数据集上进行了评估。仅需少量标注，该方法在肿瘤病例上的纤维束分割效果可与健康受试者相媲美（Dice系数=0.71），而自动方法（如TractSeg）的性能相比健康受试者显著下降（Dice系数=0.34）。该方法已作为名为atTRACTive的原型工具集成至免费软件MITK Diffusion中。与传统的基于感兴趣区域的分割相比，针对肿瘤数据的实验表明，因其分割时间更短而具有更高的效率。