A Review of MRI Acute Ischemic Stroke Lesion Segmentation

Abstract

Immediate treatment of a stroke can minimize long-term effects and even help reduce death risk. In the ischemic stroke cases, there are two zones of injury which are ischemic core and ischemic penumbra zone. The ischemic penumbra indicates the part that is located around the infarct core that is at risk of developing a brain infarction. Recently, various segmentation methods of infarct lesion from the MRI input images were developed and these methods gave a high accuracy in the extraction and detection of the infarct core. However, only some limited works have been reported to isolate the penumbra tissues and infarct core separately. The challenges exist in ischemic core identification are traditional approach prone to error, time-consuming and tedious for medical expert which could delay the treatment. In this paper, we study and analyse the segmentation algorithms for brain MRI ischemic of different categories. The focus of the review is mainly on the segmentation algorithms of infarct core with penumbra and infarct core only. We highlight the advantages and limitations alongside the discussion of the capabilities of these segmentation algorithms and its key challenges. The paper also devised a generic structure for automated stroke lesion segmentation. The performance of these algorithms was investigated by comparing different parameters of the surveyed algorithms. In addition, a new structure of the segmentation process for segmentation of penumbra is proposed by considering the challenges remains. The best accuracy for segmentation of infarct core and penumbra tissues is 82.1% whereas 99.1% for segmentation infarct core only. Meanwhile, the shortest average computational time recorded was 3.42 seconds for segmenting 10 slices of MR images. This paper presents an inclusive analysis of the discussed papers based on different categories of the segmentation algorithm. The proposed structure is important to enable a more robust and accurate assessment in clinical practice. This could be an opportunity for the medical and engineering sector to work together in designing a complete end-to-end automatic framework in detecting stroke lesion and penumbra.

Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review

“…Skull stripping removes the bone structure from a medical image to ensure that only brain tissue is considered during segmentation [43]. Different methods of performing skull stripping include morphology-based [44,45], intensity-based [46,47], and atlas-based [48] methods.…”