Deep Learning for Detection of Intracranial Aneurysms from Computed Tomography Angiography Images.
The accuracy of computed tomography angiography (CTA) image interpretation depends on the radiologist. This study aims to develop a new method for automatically detecting intracranial aneurysms from CTA images using deep learning, based on a convolutional neural network (CNN) implemented on the Deep...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 1; pp. 114 - 124 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162233253&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162233253 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2023 vid: 36 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162233253 159016481 162233253 162233253 10.1007/s10278-022-00698-5 162233253 ppf: 114 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning for Detection of Intracranial Aneurysms from Computed Tomography Angiography Images. aug: au: Liu, Xiujuan Mao, Jun Sun, Ning Yu, Xiangrong Chai, Lei Tian, Ye Wang, Jianming Liang, Jianchao Tao, Haiquan Yuan, Lihua Lu, Jiaming Wang, Yang Zhang, Bing Wu, Kaihua Wang, Yiding Chen, Mengjiao Wang, Zhishun Lu, Ligong affil: Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, 519000, Xiangzhou District, Zhuhai, Guangdong, China sug: subj: Deep Learning Utilization Cerebral Aneurysm Radiography Computed Tomography Angiography Methods Radiographic Image Interpretation, Computer-Assisted Methods Neural Networks (Computer) Human Descriptive Statistics Comparative Studies Models, Structural Sensitivity and Specificity Evaluation Predictive Value of Tests False Positive Results Diagnosis, Computer Assisted Funding Source ab: The accuracy of computed tomography angiography (CTA) image interpretation depends on the radiologist. This study aims to develop a new method for automatically detecting intracranial aneurysms from CTA images using deep learning, based on a convolutional neural network (CNN) implemented on the DeepMedic platform. Ninety CTA scans of patients with intracranial aneurysms are collected and divided into two datasets: training (80 subjects) and test (10 subjects) datasets. Subsequently, a deep learning architecture with a three-dimensional (3D) CNN model is implemented on the DeepMedic platform for the automatic segmentation and detection of intracranial aneurysms from the CTA images. The samples in the training dataset are used to train the CNN model, and those in the test dataset are used to assess the performance of the established system. Sensitivity, positive predictive value (PPV), and false positives are evaluated. The overall sensitivity and PPV of this system for detecting intracranial aneurysms from CTA images are 92.3% and 100%, respectively, and the segmentation sensitivity is 92.3%. The performance of the system in the detection of intracranial aneurysms is closely related to their size. The detection sensitivity for small intracranial aneurysms (≤ 3 mm) is 66.7%, whereas the sensitivity of detection for large (> 10 mm) and medium-sized (3–10 mm) intracranial aneurysms is 100%. The deep learning architecture with a 3D CNN model on the DeepMedic platform can reliably segment and detect intracranial aneurysms from CTA images with high sensitivity. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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