Intracranial aneurysms are a commonly occurring and life-threatening
condition, affecting approximately 3.2% of the general population.
Consequently, detecting these aneurysms plays a crucial role in their
management. Lesion detection involves the simultaneous localization and
categorization of abnormalities within medical images. In this study, we
employed the nnDetection framework, a self-configuring framework specifically
designed for 3D medical object detection, to detect and localize the 3D
coordinates of aneurysms effectively. To capture and extract diverse features
associated with aneurysms, we utilized TOF-MRA and structural MRI, both
obtained from the ADAM dataset. The performance of our proposed deep learning
model was assessed through the utilization of free-response receiver operative
characteristics for evaluation purposes. The model's weights and 3D prediction
of the bounding box of TOF-MRA are publicly available at
this https URL

使用 nnDetection 框架和 TOF-MRA 进行医学图像分析，能够有效检测和定位颅内动脉瘤，具有潜在的临床应用。

颅内动脉瘤检测和定位的神经网络检测

nnDetection for Intracranial Aneurysms Detection and Localization

The growth of unruptured intracranial aneurysms (UIAs) is a predictor of
rupture. Therefore, for further imaging surveillance and treatment planning, it
is important to be able to predict if an UIA is likely to grow based on an
initial baseline Time-of-Flight MRA (TOF-MRA). It is known that the size and
shape of UIAs are predictors of aneurysm growth and/or rupture. We perform a
feasibility study of using a mesh convolutional neural network for future UIA
growth prediction from baseline TOF-MRAs. We include 151 TOF-MRAs, with 169
UIAs where 49 UIAs were classified as growing and 120 as stable, based on the
clinical definition of growth (>1 mm increase in size in follow-up scan). UIAs
were segmented from TOF-MRAs and meshes were automatically generated. We
investigate the input of both UIA mesh only and region-of-interest (ROI) meshes
including UIA and surrounding parent vessels. We develop a classification model
to predict UIAs that will grow or remain stable. The model consisted of a mesh
convolutional neural network including additional novel input edge features of
shape index and curvedness which describe the surface topology. It was
investigated if input edge mid-point co-ordinates influenced the model
performance. The model with highest AUC (63.8%) for growth prediction was using
UIA meshes with input edge mid-point co-ordinate features (average F1 score =
62.3%, accuracy = 66.9%, sensitivity = 57.3%, specificity = 70.8%). We present
a future UIA growth prediction model based on a mesh convolutional neural
network with promising results.

本研究使用基于网格卷积神经网络的方法，通过附加形状和曲折程度等不同特征，对未破裂颅内动脉瘤的生长情况进行预测，该模型的 AUC 最高可达 63.8%，对于生长情况的预测有着良好的表现。