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Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography(TOF-MRA) has undergone drastic improvements with the advent of Deep Learning(DL). However, performances of supervised DL models heavily rely on thequantity of labeled samples, which are extremely costly to obtain. Here, wepresent a DL model for aneurysm detection that overcomes the issue with''weak'' labels: oversized annotations which are considerably faster to create.Our weak labels resulted to be four times faster to generate than theirvoxel-wise counterparts. In addition, our model leverages prior anatomicalknowledge by focusing only on plausible locations for aneurysm occurrence. Wefrst train and evaluate our model through cross-validation on an in-houseTOF-MRA dataset comprising 284 subjects (170 females / 127 healthy controls /157 patients with 198 aneurysms). On this dataset, our best model achieved asensitivity of 83%, with False Positive (FP) rate of 0.8 per patient. To assessmodel generalizability, we then participated in a challenge for aneurysmdetection with TOF-MRA data (93 patients, 20 controls, 125 aneurysms). On thepublic challenge, sensitivity was 68% (FP rate=2.5), ranking 4th/18 on the openleaderboard. We found no signifcant diference in sensitivity between aneurysmrisk-of-rupture groups (p=0.75), locations (p=0.72), or sizes (p=0.15). Data,code and model weights are released under permissive licenses. We demonstratethat weak labels and anatomical knowledge can alleviate the necessity forprohibitively expensive voxel-wise annotations.

Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge