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Purpose: Microsurgical Aneurysm Clipping Surgery (MACS) carries a high riskfor intraoperative aneurysm rupture. Automated recognition of instances whenthe aneurysm is exposed in the surgical video would be a valuable referencepoint for neuronavigation, indicating phase transitioning and more importantlydesignating moments of high risk for rupture. This article introduces the MACSdataset containing 16 surgical videos with frame-level expert annotations andproposes a learning methodology for surgical scene understanding identifyingvideo frames with the aneurysm present in the operating microscope'sfield-of-view. Methods: Despite the dataset imbalance (80% no presence, 20%presence) and developed without explicit annotations, we demonstrate theapplicability of Transformer-based deep learning architectures (MACSSwin-T,vidMACSSwin-T) to detect the aneurysm and classify MACS frames accordingly. Weevaluate the proposed models in multiple-fold cross-validation experiments withindependent sets and in an unseen set of 15 images against 10 human experts(neurosurgeons). Results: Average (across folds) accuracy of 80.8% (range78.5%-82.4%) and 87.1% (range 85.1%-91.3%) is obtained for the image- andvideo-level approach respectively, demonstrating that the models effectivelylearn the classification task. Qualitative evaluation of the models' classactivation maps show these to be localized on the aneurysm's actual location.Depending on the decision threshold, MACSWin-T achieves 66.7% to 86.7% accuracyin the unseen images, compared to 82% of human raters, with moderate to strongcorrelation.

Shifted-Windows Transformers for the Detection of Cerebral Aneurysms in Microsurgery