  Patients with Intracranial Hemorrhage (ICH) face a potentially
life-threatening condition, and patient-centered individualized treatment
remains challenging due to possible clinical complications. Deep-Learning-based
methods can efficiently analyze the routinely acquired head CTs to support the
clinical decision-making. The majority of early work focuses on the detection
and segmentation of ICH, but do not model the complex relations between ICH and
adjacent brain structures. In this work, we design a tailored object detection
method for ICH, which we unite with segmentation-grounded Scene Graph
Generation (SGG) methods to learn a holistic representation of the clinical
cerebral scene. To the best of our knowledge, this is the first application of
SGG for 3D voxel images. We evaluate our method on two head-CT datasets and
demonstrate that our model can recall up to 74% of clinically relevant
relations. This work lays the foundation towards SGG for 3D voxel data. The
generated Scene Graphs can already provide insights for the clinician, but are
also valuable for all downstream tasks as a compact and interpretable
representation.
