An Automatic Detection Method Of Cerebral Aneurysms In Time-Of-Flight Magnetic Resonance Angiography Images Based On Attention 3D U-Net

• URL: http://arxiv.org/abs/2110.13367v1
• Date: Tue, 26 Oct 2021 02:45:15 GMT
• Title: An Automatic Detection Method Of Cerebral Aneurysms In Time-Of-Flight Magnetic Resonance Angiography Images Based On Attention 3D U-Net
• Authors: Chen Geng, Meng Chen, Ruoyu Di, Dongdong Wang, Liqin Yang, Wei Xia, Yuxin Li, Daoying Geng
• Abstract summary: Subarachnoid hemorrhage caused by ruptured cerebral aneurysm often leads to fatal consequences. Time-of-flight magnetic resonance angiography is one of the most commonly used non-invasive screening techniques for cerebral aneurysm. The application of deep learning technology in aneurysm detection can effectively improve the screening effect of aneurysm.
• Score: 17.556541347902638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background:Subarachnoid hemorrhage caused by ruptured cerebral aneurysm often leads to fatal consequences.However,if the aneurysm can be found and treated during asymptomatic periods,the probability of rupture can be greatly reduced.At present,time-of-flight magnetic resonance angiography is one of the most commonly used non-invasive screening techniques for cerebral aneurysm,and the application of deep learning technology in aneurysm detection can effectively improve the screening effect of aneurysm.Existing studies have found that three-dimensional features play an important role in aneurysm detection,but they require a large amount of training data and have problems such as a high false positive rate. Methods:This paper proposed a novel method for aneurysm detection.First,a fully automatic cerebral artery segmentation algorithm without training data was used to extract the volume of interest,and then the 3D U-Net was improved by the 3D SENet module to establish an aneurysm detection model.Eventually a set of fully automated,end-to-end aneurysm detection methods have been formed. Results:A total of 231 magnetic resonance angiography image data were used in this study,among which 132 were training sets,34 were internal test sets and 65 were external test sets.The presented method obtained 97.89% sensitivity in the five-fold cross-validation and obtained 91.0% sensitivity with 2.48 false positives/case in the detection of the external test sets. Conclusions:Compared with the results of our previous studies and other studies,the method in this paper achieves a very competitive sensitivity with less training data and maintains a low false positive rate.As the only method currently using 3D U-Net for aneurysm detection,it proves the feasibility and superior performance of this network in aneurysm detection,and also explores the potential of the channel attention mechanism in this task.

Related papers

• Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet [0.5106162890866905]
  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. 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.
  arXiv  Detail & Related papers  (2023-08-06T03:28:08Z)
• Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning [14.62565592495898]
  This paper aims to study a deep learning method based on cardiac ultrasound video to assist in atrial septal defect diagnosis. We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score.
  arXiv  Detail & Related papers  (2023-06-06T16:25:29Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Automatic Diagnosis of Carotid Atherosclerosis Using a Portable Freehand 3D Ultrasound Imaging System [18.73291257371106]
  A total of 127 3D carotid artery scans were acquired using a portable 3D US system. A U-Net segmentation network was applied to extract the carotid artery on 2D transverse frame. A novel 3D reconstruction algorithm using fast dot projection (FDP) method with position regularization was proposed to reconstruct the carotid artery volume.
  arXiv  Detail & Related papers  (2023-01-08T17:35:36Z)
• Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data [53.122045119395594]
  We study how unhealthy samples within the training data affect anomaly detection performance for brain MRI-scans. We evaluate a method to identify falsely labeled samples directly during training based on the reconstruction error of the AE.
  arXiv  Detail & Related papers  (2022-04-12T13:05:18Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Revealing unforeseen diagnostic image features with deep learning by detecting cardiovascular diseases from apical four-chamber ultrasounds [0.0]
  We developed a deep learning (DL) method for automated detection of impaired left ventricular (LV) function and aortic valve (AV) regurgitation from apical four-chamber ultrasound cineloops. Two convolutional neural networks (CNNs) were trained separately to classify the respective disease cases against normal cases. Overall classification accuracy of the impaired LV function detection model was 86%, and that of the AV regurgitation detection model was 83%.
  arXiv  Detail & Related papers  (2021-10-25T12:53:45Z)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• Automatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning [0.0]
  We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences. This method uses both the three-dimensional spatial information and temporal information obtained from the early-phase of the dynamic acquisition.
  arXiv  Detail & Related papers  (2021-02-07T22:03:39Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.