Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel
  Segmentation
Patryk Rygiel Dieuwertje Alblas Christoph Brune Kak Khee Yeung Jelmer M. Wolterink
Personalized 3D vascular models can aid in a range of diagnostic, prognostic,
and treatment-planning tasks relevant to cardiovascular disease management.
Deep learning provides a means to automatically obtain such models. Ideally, a
user should have control over the exact region of interest (ROI) to be included
in a vascular model, and the model should be watertight and highly accurate. To
this end, we propose a combination of a global controller leveraging voxel mask
segmentations to provide boundary conditions for vessels of interest to a
local, iterative vessel segmentation model. We introduce the preservation of
scale- and rotational symmetries in the local segmentation model, leading to
generalisation to vessels of unseen sizes and orientations. Combined with the
global controller, this enables flexible 3D vascular model building, without
additional retraining. We demonstrate the potential of our method on a dataset
containing abdominal aortic aneurysms (AAAs). Our method performs on par with a
state-of-the-art segmentation model in the segmentation of AAAs, iliac arteries
and renal arteries, while providing a watertight, smooth surface segmentation.
Moreover, we demonstrate that by adapting the global controller, we can easily
extend vessel sections in the 3D model.