Automatic detection and quantification of acute cerebral infarct by fuzzy clustering and histographic characterization on diffusion weighted MR imaging and apparent diffusion coefficient map.
Jang-Zern TsaiSyu-Jyun PengYu-Wei ChenKuo-Wei WangHsiao-Kuang WuYun-Yu LinYing-Ying LeeChi-Jen ChenHuey-Juan LinEric Edward SmithPoh-Shiow YehYue-Loong HsinPublished in: BioMed research international (2014)
Determination of the volumes of acute cerebral infarct in the magnetic resonance imaging harbors prognostic values. However, semiautomatic method of segmentation is time-consuming and with high interrater variability. Using diffusion weighted imaging and apparent diffusion coefficient map from patients with acute infarction in 10 days, we aimed to develop a fully automatic algorithm to measure infarct volume. It includes an unsupervised classification with fuzzy C-means clustering determination of the histographic distribution, defining self-adjusted intensity thresholds. The proposed method attained high agreement with the semiautomatic method, with similarity index 89.9 ± 6.5%, in detecting cerebral infarct lesions from 22 acute stroke patients. We demonstrated the accuracy of the proposed computer-assisted prompt segmentation method, which appeared promising to replace the laborious, time-consuming, and operator-dependent semiautomatic segmentation.
Keyphrases
- diffusion weighted imaging
- deep learning
- contrast enhanced
- diffusion weighted
- magnetic resonance imaging
- machine learning
- liver failure
- convolutional neural network
- acute myocardial infarction
- respiratory failure
- subarachnoid hemorrhage
- computed tomography
- neural network
- drug induced
- aortic dissection
- magnetic resonance
- solid phase extraction
- cerebral ischemia
- molecularly imprinted
- high intensity
- rna seq
- coronary artery disease
- mass spectrometry
- cerebral blood flow
- high resolution
- acute respiratory distress syndrome
- atrial fibrillation