Automatic Stroke Lesions Segmentation in Diffusion-Weighted MRI

TL;DR

This paper evaluates semi-automatic segmentation techniques for stroke lesion detection in diffusion-weighted MRI, comparing methods like Otsu, Fuzzy C-means, Hill-climbing, and Growcut, validated by accuracy, sensitivity, and specificity.

Contribution

It introduces a semi-automatic segmentation approach using FLAIR MRI as a gold standard and systematically compares multiple algorithms for stroke lesion segmentation.

Findings

Selected segmentation methods demonstrate high accuracy and reliability.

Validation shows effective differentiation of stroke lesions.

The approach reduces human error in clinical segmentation.

Abstract

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.