Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review

Malik, Mishaim; Chong, Benjamin; Fernandez, Justin; Shim, Vickie; Kasabov, Nikola Kirilov; Wang, Alan

doi:10.3390/bioengineering11010086

Cited by 2 publications

(1 citation statement)

References 108 publications

(149 reference statements)

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“…In order to benchmark stroke lesion segmentation algorithms under non-domain adaptation scenarios, we refer to the dataset collated in this study (Malik et al, 2024 ). As shown in Table 7 , eight stroke lesion segmentation algorithms from the ATLAS project were employed.…”

Section: Resultsmentioning

confidence: 99%

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review

Yanzhen,

Song,

Wanping

et al. 2024

Front. Neurosci.

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IntroductionBrain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation.MethodsThis review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation.ResultsA total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods.ConclusionAlthough various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

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Section: Resultsmentioning

confidence: 99%

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review

Yanzhen,

Song,

Wanping

et al. 2024

Front. Neurosci.

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Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis

Fernandes,

Cardoso,

Comesaña-Campos

et al. 2024

Sensors

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Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.

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Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review

Cited by 2 publications

References 108 publications

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review

Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis

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