  Accurate identification and quantification of unruptured intracranial
aneurysms (UIAs) is crucial for the risk assessment and treatment of this
cerebrovascular disorder. Current 2D manual assessment on 3D magnetic resonance
angiography (MRA) is suboptimal and time-consuming. In addition, one major
issue in medical image segmentation is the need for large well-annotated data,
which can be expensive to obtain. Techniques that mitigate this requirement,
such as weakly supervised learning with coarse labels are highly desirable. In
the paper, we propose FocalSegNet, a novel 3D focal modulation UNet, to detect
an aneurysm and offer an initial, coarse segmentation of it from time-of-flight
MRA image patches, which is further refined with a dense conditional random
field (CRF) post-processing layer to produce a final segmentation map. We
trained and evaluated our model on a public dataset, and in terms of UIA
detection, our model showed a low false-positive rate of 0.21 and a high
sensitivity of 0.80. For voxel-wise aneurysm segmentation, we achieved a Dice
score of 0.68 and a 95% Hausdorff distance of ~0.95 mm, demonstrating its
strong performance. We evaluated our algorithms against the state-of-the-art 3D
Residual-UNet and Swin-UNETR, and illustrated the superior performance of our
proposed FocalSegNet, highlighting the advantages of employing focal modulation
for this task.
