Automatic Stroke Lesions Segmentation in Diffusion-Weighted MRI
This work addresses the need for reliable stroke lesion segmentation in clinical settings, but it is incremental as it applies existing methods to a new dataset without introducing novel techniques.
The study tackled the problem of segmenting stroke lesions in diffusion-weighted MRI by evaluating several existing segmentation algorithms against a semi-automatic gold standard, achieving effectiveness in accuracy, sensitivity, and specificity through cross-validation.
Diffusion-Weighted Magnetic Resonance Imaging (DWI) is widely used for early cerebral infarct detection caused by ischemic stroke. Manual segmentation is done by a radiologist as a common clinical process, nonetheless, challenges of cerebral infarct segmentation come from low resolution and uncertain boundaries. Many segmentation techniques have been proposed and proved by manual segmentation as gold standard. In order to reduce human error in research operation and clinical process, we adopt a semi-automatic segmentation as gold standard using Fluid-Attenuated Inversion-Recovery (FLAIR) Magnetic Resonance Image (MRI) from the same patient under controlled environment. Extensive testing is performed on popular segmentation algorithms including Otsu method, Fuzzy C-means, Hill-climbing based segmentation, and Growcut. The selected segmentation techniques have been validated by accuracy, sensitivity, and specificity using leave-one-out cross-validation to determine the possibility of each techniques first then maximizes the accuracy from the training set. Our experimental results demonstrate the effectiveness of selected methods.