Intracranial aneurysm detection based on 3D point cloud object detection method

Abstract

Intracranial aneurysm (IA) is a serious threat to human health and can lead to subarachnoid hemorrhage and other serious consequences. If IAs can be detected in advance and treated before rupture, it will greatly reduce the harm of IAs to patients. Since brain arteries are 3D (three-dimensional) structures, point cloud methods can directly process 3D data, which is crucial for tasks that require spatial understanding, such as object detection. The 3D point cloud object detection methods Point-Voxel Feature Set Abstraction (PV-RCNN), Part-Aware and Part-Aggregation (PartA2), and Sparsely Embedded Convolutional Detection (SECOND) were applied to the detection of IAs. The object detection model was trained and tested on the public dataset IntrA. The results indicate that the trained model can be used for the detection and location of IAs and high recall values have been obtained on the testing set. This work also provides important metrics for evaluating object detection models, including average precisions (APs), recall values, and object detection results on the testing set, that is, predicted bounding boxes and corresponding confidence scores. In terms of detection results, the IA detection results of PV-RCNN are the best of these three methods by leveraging both point cloud and image information. The object detection method of 3D point cloud can be integrated into the medical imaging post-processing system and can be used as a subsequent module of the 3D reconstruction module.

Keywords

• Intracranial aneurysms
• object detection
• 3D point cloud
• convolutional neural network
• artificial intelligence
• Dr Jin Zhongmin, Southwest Jiaotong University, Chengdu, China