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.

翻译：准确识别与量化未破裂颅内动脉瘤（UIAs）对于此类脑血管疾病的风险评估和治疗至关重要。当前基于三维磁共振血管造影（MRA）的二维手动评估方法存在精度不足且耗时的问题。此外，医学图像分割面临的主要挑战之一是需要大量精确标注数据，而此类数据获取成本高昂。能够缓解这一需求的技术（如利用粗糙标签的弱监督学习方法）备受期待。本文提出FocalSegNet——一种新型三维焦点调制UNet模型，用于从飞行时间MRA图像块中检测动脉瘤并提供初始粗糙分割结果，随后通过密集条件随机场（CRF）后处理层进行精细化处理，生成最终分割图像。我们在公开数据集上对模型进行训练与评估：在UIA检测方面，模型实现了0.21的低假阳性率和0.80的高灵敏度；在体素级动脉瘤分割中，Dice系数达0.68，95% Hausdorff距离约为0.95毫米，展现了优异的性能。通过将算法与当前最先进的三维Residual-UNet及Swin-UNETR进行对比，我们证实了FocalSegNet的优越性能，凸显了焦点调制在该任务中的优势。