Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography
| Autor: | Sachin Jambawalikar, Jack Grinband, Michael Z. Liu, Joseph N. Stember, Christopher G. Filippi, Philip M. Meyers, Danielle Stember, Peter Chang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Convolutional neural network Magnetic resonance angiography Article 030218 nuclear medicine & medical imaging Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Aneurysm Image Interpretation Computer-Assisted medicine Humans Radiology Nuclear Medicine and imaging Rupture risk cardiovascular diseases Retrospective Studies Radiological and Ultrasound Technology medicine.diagnostic_test Receiver operating characteristic business.industry Deep learning Pattern recognition Intracranial Aneurysm medicine.disease Computer Science Applications Cerebral Angiography Maximum intensity projection cardiovascular system Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery Magnetic Resonance Angiography |
| Zdroj: | J Digit Imaging |
| Popis: | 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 mm(2) 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. |
| Databáze: | OpenAIRE |
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