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多种应用（包括手术规划和纤维束特异性分析）至关重要。监督式机器学习模型已能自动完成该任务并达到当前最优性能。然而，这些模型主要基于健康受试者数据训练，在面对（如脑肿瘤引起的）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场景，为此通常采用耗时且极具挑战性的手动勾画目标纤维束。我们提出基于半自动熵值主动学习的方法，用于快速直观地从包含数百万条流线的全脑纤维束成像中分割白质纤维束。该方法在人类连接组计划21名公开健康受试者数据及内部数据集（10例神经外科病例）上进行了评估。仅需少量标注，所提方法即可实现肿瘤病例的纤维束分割能力与健康受试者相当（Dice系数=0.71），而TractSeg等自动方法的性能相较健康受试者显著下降（Dice系数=0.34）。该方法以名为atTRACTive的原型形式集成于免费软件MITK Diffusion中。针对肿瘤数据的实验表明，与传统基于感兴趣区域的分割相比，该方法因缩短分割时间而具有更高效率。