Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography.
Aneurysm size correlates with rupture risk and is important for treatment planning. User annotation of aneurysm size is slow and tedious, particularly for large data sets. Geometric shortcuts to compute size have been shown to be inaccurate, particularly for nonstandard aneurysm geometries. To devel...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 5; pp. 808 - 816 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2019
|
| 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=138543059&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138543059 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2019 vid: 32 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138543059 138543059 144082985 138543059 10.1007/s10278-018-0162-z 138543059 ppf: 808 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography. aug: au: Stember, Joseph N. Chang, Peter Stember, Danielle M. Liu, Michael Grinband, Jack Filippi, Christopher G. Meyers, Philip Jambawalikar, Sachin affil: Radiology, Columbia University Medical Center, 622 West 168th Street, PB 1-301, New York, NY, USA sug: subj: Cerebral Aneurysm Diagnosis Cerebral Aneurysm Pathology Magnetic Resonance Angiography Neural Networks (Computer) Human Descriptive Statistics ROC Curve Image Processing, Computer Assisted ab: Aneurysm size correlates with rupture risk and is important for treatment planning. User annotation of aneurysm size is slow and tedious, particularly for large data sets. Geometric shortcuts to compute size have been shown to be inaccurate, particularly for nonstandard aneurysm geometries. To develop and train a convolutional neural network (CNN) to detect and measure cerebral aneurysms from magnetic resonance angiography (MRA) automatically and without geometric shortcuts. In step 1, a CNN based on the U-net architecture was trained on 250 MRA maximum intensity projection (MIP) images, then applied to a testing set. In step 2, the trained CNN was applied to a separate set of 14 basilar tip aneurysms for size prediction. Step 1—the CNN successfully identified aneurysms in 85/86 (98.8% of) testing set cases, with a receiver operating characteristic (ROC) area-under-the-curve of 0.87. Step 2—automated basilar tip aneurysm linear size differed from radiologist-traced aneurysm size on average by 2.01 mm, or 30%. The CNN aneurysm area differed from radiologist-derived area on average by 8.1 mm2 or 27%. CNN correctly predicted the area trend for the set of aneurysms. This approach is to our knowledge the first using CNNs to derive aneurysm size. In particular, we demonstrate the clinically pertinent application of computing maximal aneurysm one-dimensional size and two-dimensional area. We propose that future work can apply this to facilitate pre-treatment planning and possibly identify previously missed aneurysms in retrospective assessment. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|