Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT

Xu, Jun; Zhang, Rongguo; Zhou, Zijian; Wu, Chunxue; Gong, Qiang; Zhang, Huiling; Wu, Shuang; Wu, Gang; Deng, Yufeng; Chen, Xia; Ma, Jun

doi:10.3389/fnins.2020.541817

Cited by 40 publications

(30 citation statements)

References 25 publications

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“…Several different deep learning models have been developed for ICH automated quantification. Research has supported that the volume of ICH segmented by deep learning models, which is faster than manual CTP, yields similar results to CTP; [ 18 , 19 , 28 – 30 ]this is consistent with our present results.…”

Section: Discussionsupporting

confidence: 92%

“…The deep learning algorithm labels a target with a pixel-wise precise boundary and segments it.The volume of each pixel was calculated by combining CT slice thickness, and the hemorrhage volume was calculated by accumulating all the volumes of pixels in the hemorrhage region [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…Accordingly, its network can learn by analyzing the training data and make predictions as new data are entered, requiring little manual engineering [ 15 , 17 ]. Some deep learning-based AI diagnosis systems have been developed to detect and segment cerebral hemorrhage,[ 18 , 19 ] but the accuracy of these algorithms and their practical clinical value require further external verification. IVH has been associated with mortality rates as high as 50–75% [ 20 , 21 ] and increasing the accuracy of the definitions of IVH volume could improve its ability to predict outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement

et al. 2021

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Background Accurate measurement of hemorrhage volume is critical for both the prediction of prognosis and the selection of appropriate clinical treatment after spontaneous intracerebral hemorrhage (ICH). This study aimed to evaluate the performance and accuracy of a deep learning-based automated segmentation algorithm in segmenting spontaneous intracerebral hemorrhage (ICH) volume either with or without intraventricular hemorrhage (IVH) extension. We compared this automated pipeline with two manual segmentation techniques. Methods We retrospectively reviewed 105 patients with acute spontaneous ICH. Depending on the presence of IVH extension, patients were divided into two groups: ICH without (n = 56) and with IVH (n = 49). ICH volume of the two groups were segmented and measured using a deep learning-based artificial intelligence (AI) diagnostic system and computed tomography-based planimetry (CTP), and the ABC/2 score were used to measure hemorrhage volume in the ICH without IVH group. Correlations and agreement analyses were used to analyze the differences in volume and length of processing time among the three segmentation approaches. Results In the ICH without IVH group, the ICH volumes measured using AI and the ABC/2 score were comparable to CTP segmentation. Strong correlations were observed among the three segmentation methods (r = 0.994, 0.976, 0.974; P < 0.001; concordance correlation coefficient [CCC] = 0.993, 0.968, 0.967). But the absolute error of the ICH volume measured by the ABC/2 score was greater than that of the algorithm (P < 0.05). In the ICH with IVH group, there is no significant differences were found between algorithm and CTP(P = 0.614). The correlation and agreement between CTP and AI were strong (r = 0.996, P < 0.001; CCC = 0.996). The AI segmentation took a significantly shorter amount of time than CTP (P < 0.001), but was slightly longer than ABC/2 score technique (P = 0.002). Conclusions The deep learning-based AI diagnostic system accurately quantified volumes of acute spontaneous ICH with high fidelity and greater efficiency compared to the CTP measurement and more accurately than the ABC/2 scores. We believe this is a promising tool to help physicians achieve precise ICH quantification in practice.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement

et al. 2021

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“…Recently, automated image analysis software based on deep learning algorithms have been developed for detection and volume quantification of ICH [14][15][16][17][18]. Implementing such automated imaging analysis tools in routine healthcare may improve early detection by prioritizing among radiological exams and reduce missed ICH diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of automated intracerebral hemorrhage volume measurement on non-contrast computed tomography: a Swedish Stroke Register cohort study

et al. 2022

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Purpose Hematoma volume is the strongest predictor of patient outcome after intracerebral hemorrhage (ICH). The aim of this study was to validate novel fully automated software for quantification of ICH volume on non-contrast computed tomography (CT). Methods The population was defined from the Swedish Stroke Register (RS) and included all patients with an ICH diagnosis during 2016–2019 in Region Skåne. Hemorrhage volume on their initial head CT was measured using ABC/2 and manual segmentation (Sectra IDS7 volume measurement tool) and the automated volume quantification tool (qER–NCCT) by Qure.ai. The first 500 were examined by two independent readers. Results A total of 1649 ICH patients were included. The qER–NCCT had 97% sensitivity in identifying ICH. In total, there was excellent agreement between volumetric measurements of ICH volumes by qER–NCCT and manual segmentation by interclass correlation (ICC = 0.96), and good agreement (ICC = 0.86) between qER–NCCT and ABC/2 method. The qER–NCCT showed volume underestimation, mainly in large (> 30 ml) heterogenous hemorrhages. Interrater agreement by (ICC) was 0.996 (95% CI: 0.99–1.00) for manual segmentation. Conclusion Our study showed excellent agreement in volume quantification between the fully automated software qER–NCCT and manual segmentation of ICH on NCCT. The qER–NCCT would be an important additive tool by aiding in early diagnostics and prognostication for patients with ICH and in provide volumetry on a population-wide level. Further refinement of the software should address the underestimation of ICH volume seen in a portion of large, heterogenous, irregularly shaped ICHs.

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“… Zhao et al (2021) applied the nnU-Net framework to the segmentation and volume calculation of ICH and peripheral oedema. Xu et al (2021) evaluated Dense U-Net framework for the segmentation and quantification of ICH, EDH (extradural hemorrhage) and SDH (subdural hemorrhage). Rava et al (2021) evaluated the Canon automatic stroke detection system and the automatic ICH segmentation tool in Vitrea and investigated the performance of the system in ICH detection and the effect of ICH volume on the detection performance of the system.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based computed tomography image segmentation and volume measurement of intracerebral hemorrhage

Chen

Zhang

et al. 2022

Front. Neurosci.

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The study aims to enhance the accuracy and practicability of CT image segmentation and volume measurement of ICH by using deep learning technology. A dataset including the brain CT images and clinical data of 1,027 patients with spontaneous ICHs treated from January 2010 to December 2020 were retrospectively analyzed, and a deep segmentation network (AttFocusNet) integrating the focus structure and the attention gate (AG) mechanism is proposed to enable automatic, accurate CT image segmentation and volume measurement of ICHs. In internal validation set, experimental results showed that AttFocusNet achieved a Dice coefficient of 0.908, an intersection-over-union (IoU) of 0.874, a sensitivity of 0.913, a positive predictive value (PPV) of 0.957, and a 95% Hausdorff distance (HD95) (mm) of 5.960. The intraclass correlation coefficient (ICC) of the ICH volume measurement between AttFocusNet and the ground truth was 0.997. The average time of per case achieved by AttFocusNet, Coniglobus formula and manual segmentation is 5.6, 47.7, and 170.1 s. In the two external validation sets, AttFocusNet achieved a Dice coefficient of 0.889 and 0.911, respectively, an IoU of 0.800 and 0.836, respectively, a sensitivity of 0.817 and 0.849, respectively, a PPV of 0.976 and 0.981, respectively, and a HD95 of 5.331 and 4.220, respectively. The ICC of the ICH volume measurement between AttFocusNet and the ground truth were 0.939 and 0.956, respectively. The proposed segmentation network AttFocusNet significantly outperforms the Coniglobus formula in terms of ICH segmentation and volume measurement by acquiring measurement results closer to the true ICH volume and significantly reducing the clinical workload.

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Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT

Cited by 40 publications

References 25 publications

Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement

Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement

Accuracy of automated intracerebral hemorrhage volume measurement on non-contrast computed tomography: a Swedish Stroke Register cohort study

Deep learning-based computed tomography image segmentation and volume measurement of intracerebral hemorrhage

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