Abstract: Semi-automatic White Matter Tract Segmentation using Active Learning atTRACTive

Robin Peretzke, Klaus H. Maier-Hein, Jonas Bohn, Yannick Kirchhoff, Saikat Roy, Sabrina Oberli-Palma, Daniela Becker, Pavlina Lenga, Peter Neher

Abstract: Accurately identifying white matter tracts in medical images is essential for various applications, including surgery planning. 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 employed.We propose semi-automatic entropy-based active learning for quick and intuitive segmentation of tracts from 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, this approach enables segmenting tracts on tumor cases comparable to healthy subjects (dice = 0.71), while the performance of automatic methods dropped substantially (dice = 0.34). The method named atTRACTive is implemented in the software MITK Diffusion. Manual experiments on tumor data showed higher efficiency than traditional ROI-based segmentation [1].