Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema

Zhao, Xiaoli; Chen, Kaixing; Wu, Ge; Zhang, Guyue; Zhou, Xin; Lv, Chuanfeng; Wu, Shiman; Chen, Yun; Yao, Zhenwei

doi:10.1007/s00330-020-07558-2

Cited by 44 publications

(43 citation statements)

References 41 publications

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“…Deep learning methods based on convolutional neural networks have become another option for automatic PHE segmentation. Zhao et al (58) developed a deep learning model based on an U-Net for PHE segmentation. However, the best dice value was only 0.71.…”

Section: Measurement Of Phementioning

confidence: 99%

“…Zhao et al. ( 58 ) developed a deep learning model based on an U-Net for PHE segmentation. However, the best dice value was only 0.71.…”

Section: Measurement Of Phementioning

confidence: 99%

“…However, the best dice value was only 0.71. These findings indicate that automatic PHE segmentation is considerably more difficult than hematoma segmentation because of the lower clarity of PHE on CT scans ( 58 , 59 ), necessitating refinement of the performance of automatic PHE segmentation.…”

Section: Measurement Of Phementioning

confidence: 99%

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Perihematomal Edema After Intracerebral Hemorrhage: An Update on Pathogenesis, Risk Factors, and Therapeutic Advances

Chen

Chang

et al. 2021

Front. Immunol.

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Intracerebral hemorrhage (ICH) has one of the worst prognoses among patients with stroke. Surgical measures have been adopted to relieve the mass effect of the hematoma, and developing targeted therapy against secondary brain injury (SBI) after ICH is equally essential. Numerous preclinical and clinical studies have demonstrated that perihematomal edema (PHE) is a quantifiable marker of SBI after ICH and is associated with a poor prognosis. Thus, PHE has been considered a promising therapeutic target for ICH. However, the findings derived from existing studies on PHE are disparate and unclear. Therefore, it is necessary to classify, compare, and summarize the existing studies on PHE. In this review, we describe the growth characteristics and relevant underlying mechanism of PHE, analyze the contributions of different risk factors to PHE, present the potential impact of PHE on patient outcomes, and discuss the currently available therapeutic strategies.

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Section: Measurement Of Phementioning

confidence: 99%

“…Zhao et al. ( 58 ) developed a deep learning model based on an U-Net for PHE segmentation. However, the best dice value was only 0.71.…”

Section: Measurement Of Phementioning

confidence: 99%

See 1 more Smart Citation

Perihematomal Edema After Intracerebral Hemorrhage: An Update on Pathogenesis, Risk Factors, and Therapeutic Advances

Chen

Chang

et al. 2021

Front. Immunol.

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“…As segmentation models began to show stronger performance, interest in volumetric analysis of intracranial hemorrhage increased. Some studies focused only on computing total intracranial hemorrhage ( 116 , 118 ) while others computed separate volumetric outputs for different hemorrhage subtypes ( 117 , 119 , 120 ). While the multiclass volumetric studies demonstrated promising results, many grouped multiple pathoanatomic lesion subtypes into the same category, although different subtypes often require very different clinical management steps.…”

Section: Intracranial Hemorrhage Detectionmentioning

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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“…CNNs can also be applied to vascular diseases in the craniofacial field. Several studies have reported that AI algorithms showed excellent performance in automatic segmentation of areas affected by intracranial hemorrhage using brain CT images and in measurements of hemorrhage volume [26][27][28][29]. The possibility of accurate automatic segmentation of the extent and volume of vascular tumors or malformations can be considered, even for vascular anomalies in the head and neck area [30].…”

Section: Convolutional Neural Network and Craniofacial Surgerymentioning

confidence: 99%

Potential role of artificial intelligence in craniofacial surgery

Ryu

Chung

Choi

2021

Arch Craniofac Surg

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The field of artificial intelligence (AI) is rapidly advancing, and AI models are increasingly applied in the medical field, especially in medical imaging, pathology, natural language processing, and biosignal analysis. On the basis of these advances, telemedicine, which allows people to receive medical services outside of hospitals or clinics, is also developing in many countries. The mechanisms of deep learning used in medical AI include convolutional neural networks, residual neural networks, and generative adversarial networks. Herein, we investigate the possibility of using these AI methods in the field of craniofacial surgery, with potential applications including craniofacial trauma, congenital anomalies, and cosmetic surgery.

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Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema

Cited by 44 publications

References 41 publications

Perihematomal Edema After Intracerebral Hemorrhage: An Update on Pathogenesis, Risk Factors, and Therapeutic Advances

Perihematomal Edema After Intracerebral Hemorrhage: An Update on Pathogenesis, Risk Factors, and Therapeutic Advances

Computational Approaches for Acute Traumatic Brain Injury Image Recognition

Potential role of artificial intelligence in craniofacial surgery

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