Abstract #0895

Feasibility study on automated white matter tract segmentation in neurosurgical pre-operative planning

Daniel Güllmar¹, Rotraud Neumann², Jakob Wasserthal³, Jan Walter⁴, Ulf KM Teichgräber⁵, Thomas E Mayer², and Jürgen R Reichenbach^1,6

¹Medical Physics Group, Inst. of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany, ²Section of Neuroradiology, Inst. of Diagnostic and Interv. Radiology, Jena University Hospital, Jena, Germany, ³Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany, ⁴Department of Neurosurgery, Jena University Hospital, Jena, Germany, ⁵Inst. of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany, ⁶Michel-Stifel-Center-Jena for Data-Driven and Simulation Science, Friedrich-Schiller-University Jena, Jena, Germany

In neuro-surgical preoperative planning of extirpation of large tumors it is important to locate the paths of critical cerebral nerve fiber bundles (e.g. corticospinal-tract). Manual fiber bundle selection is elaborate, requires expert knowledge and is prone to user errors. Therefore, in this study a fully automatic pipeline for white matter bundle segmentation was setup, incorporating recently published white matter bundle segmentation based using DNN, and tested with 12 patients suffering from large brain lesions. In all cases the position of the corticospinal tracts was evaluated as plausible, although in at least one hemisphere this tract was affected by the lesion.

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