Deep Learning for Hemorrhagic Lesion Detection and Segmentation on Brain CT Images

Lü, Li; Meng, Wei; Liu, Bo; Atchaneeyasakul, Kunakorn; Zhou, Fugen; Pan, Zehao; Kumar, Shimran A.; Zhang, Jason Y.; Pu, Yuehua; Liebeskind, David S; Scalzo, Fabien

doi:10.1109/jbhi.2020.3028243

Cited by 99 publications

(57 citation statements)

References 32 publications

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“…There is a considerable body of work focusing on the algorithmic detection and segmentation of acute ischemic stroke (92,103,(155)(156)(157)(158)(159)(160)(161), although not specifically within the context of TBI. As with computational approaches to image recognition in acute TBI, older studies used rule-based and traditional machine learning techniques (162,163), and descriptions of deep learning approaches to this problem have appeared only in the past several years.…”

Section: Strokementioning

confidence: 99%

Computational Approaches for Acute Traumatic Brain Injury Image Recognition

Lin

Yuh

2022

Front. Neurol.

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In recent years, there have been major advances in deep learning algorithms for image recognition in traumatic brain injury (TBI). Interest in this area has increased due to the potential for greater objectivity, reduced interpretation times and, ultimately, higher accuracy. Triage algorithms that can re-order radiological reading queues have been developed, using classification to prioritize exams with suspected critical findings. Localization models move a step further to capture more granular information such as the location and, in some cases, size and subtype, of intracranial hematomas that could aid in neurosurgical management decisions. In addition to the potential to improve the clinical management of TBI patients, the use of algorithms for the interpretation of medical images may play a transformative role in enabling the integration of medical images into precision medicine. Acute TBI is one practical example that can illustrate the application of deep learning to medical imaging. This review provides an overview of computational approaches that have been proposed for the detection and characterization of acute TBI imaging abnormalities, including intracranial hemorrhage, skull fractures, intracranial mass effect, and stroke.

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Section: Strokementioning

confidence: 99%

Computational Approaches for Acute Traumatic Brain Injury Image Recognition

Lin

Yuh

2022

Front. Neurol.

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“…Experiments are conducted to add a symmetrical constraint by using flipped images as input [166]. Arab et al, developed and evaluated an automated DL method with CNN and deep supervision CNN for precise hematoma (blood clot) segmentation and volumetric quantification in CT images [167].…”

Section: Hemorrhagic Strokementioning

confidence: 99%

A Review on Computer Aided Diagnosis of Acute Brain Stroke

Inamdar

Raghavendra

Gudigar

et al. 2021

Sensors

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Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta–Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.

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“…In clear the Artificial Intelligence (AI) acts as a major stream making machine language a subset in which deep learning acts as a specific subset. Artificial intelligence involves developing machines [12] by exhibiting intelligence. Machine learning helps in creating the algorithms using the insights of the data.…”

Section: Deep Learningmentioning

confidence: 99%

Early Detection of Hemorrhagic Stroke Using a Lightweight Deep Learning Neural Network Model

Vamsi¹,

Bhattacharyya²,

Midhunchakkravarthy³

et al. 2021

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In present days, the major disease affecting people all across the world is “Cerebrovascular Stroke”. Computed tomographic (CT) images play a crucial role in identifying hemorrhagic strokes. It also helps in understanding the impact of damage caused in the brain cells more accurately. The existing research work is implemented on the Graphical Processing Unit (GPU’s) for stroke segmentation using heavyweight convolutions that require more processing time for diagnosis and increases the model's cost. Deep learning techniques with VGG-16 architecture and Random Forest algorithm are implemented for detecting hemorrhagic stroke using brain CT images under segmentation. A two-step light-weighted convolution model is proposed by using the data collected from multiple- repositories to inscribe this constraint. In the first step, the input CT images are given to VGG-16 architecture and in step two, data frames are given to random forest for stroke segmentation with three levels of classes. In this paper, we explore various training time values in the detection of stroke that reduces when compared with existing heavyweight models. Experimental results have shown that when compared to other existing architectures, our hybrid model VGG-16 and random forest achieved increased results obtained are dice coefficient with 72.92 and accuracy with 97.81% which shows promising results.

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Deep Learning for Hemorrhagic Lesion Detection and Segmentation on Brain CT Images

Cited by 99 publications

References 32 publications

Computational Approaches for Acute Traumatic Brain Injury Image Recognition

Computational Approaches for Acute Traumatic Brain Injury Image Recognition

A Review on Computer Aided Diagnosis of Acute Brain Stroke

Early Detection of Hemorrhagic Stroke Using a Lightweight Deep Learning Neural Network Model

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