1D CNN-Based Intracranial Aneurysms Detection in 3D TOF-MRA

Abstract

How to automatically detect intracranial aneurysms from Three-Dimension Time of Flight Magnetic Resonance Angiography (3D TOF MRA) images is a typical 3D image classification problem. Currently, the commonly used method is the Maximum Intensity Projection- (MIP-) based way. It transfers 3D classification into 2D case by projecting the 3D patch into 2D planes along different directions on the basis of voxel’s intensity. After then, the 2D Convolutional Neural Network (CNN) is established to do classification. It has been shown that the MIP-based method can reduce the demands for the samples and increase the computation efficiency. Meanwhile, the accuracy is comparable with that of 3D image classification. Inspired by the strategy of MIP, we want to further reduce the demands for samples and accelerate the training by transferring the 2D image classification into 1D case, i.e., we want to generate the 1D vectors from the MIP images and then establish a 1D CNN to do intracranial aneurysm detection and classification for 3D TOF MRA image. Specifically, our method first extracts a series of patches as the Region of Interests (ROIs) along the blood vessels from the original 3D TOF MRA 3D image. The corresponding MIP images of each ROI will be obtained through maximum intensity projecting. Then, we generate a series of 1D vectors by accumulating each MIP image along different directions. Meanwhile, a 1D CNN is established to detect aneurysms, in which, the input is the obtained 1D vectors and the output is the binary classification result denoting whether there are intracranial aneurysms in the considered patch. Generally, compared with 2D- and 3D-CNN, the 1D CNN-based way greatly accelerates the training and shows stronger robustness in the case of fewer samples. The efficiency of the proposed method outperforms the 2D CNN about 10 times in CPU training. Yet, their accuracies are close.

DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images

“…The main existing methods are based on the maximal intensity projection (MIP) algorithm [ 24 ], which projects 3D images onto two-dimensional (2D) images in different directions according to the voxel intensity, following which a 2D CNN is constructed for feature detection [ 25 , 26 ]. Jin et al [ 27 ] introduced a bidirectional convolutional long short-term memory module based on the U-Net architecture, for learning the spatial and temporal information of aneurysms in different 2D digital subtraction angiography (DSA) sequences for end-to-end training.…”

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis

“…Figure 1 shows that overall, 1736 studies met the search criteria and 99 potentially eligible full-text articles were assessed. Forty-three studies ranging from October 2004 to August 2021 were included 21–63. The total number of patient cases used for both training and testing was 18 143, and of these 10 625 patients had aneurysms, with a combined total of 12 990 aneurysms (datasets that were used across different studies were only included once).…”

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

“…The boundary between regression and classification is not clearly defined as continuous values in regression can be transformed into a classification problem through thresholding or discretization techniques. At present, various supervised learning algorithms, such as SVM, ,,,− linear discriminant analysis (LDA), ,,,,,,− random forest (RF), ,,,, and neural network (NN), ,,− have been extensively utilized for disease diagnosis.…”