Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain

Chan, Tao

doi:10.1016/j.compmedimag.2007.02.010

Cited by 119 publications

(70 citation statements)

References 12 publications

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“…11,[26][27][28] Nowinski and colleagues have compared agreement of different segmentation algorithms with respective manual segmentations. CCCs for algorithms based on clustering, graph theory, and modified thresholding were 0.87, 0.91, and 0.97, respectively, and they are comparable to the CCCs calculated for our segmentation algorithm.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage

Scherer

Cordes

Younsi

et al. 2016

Stroke

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Background and Purpose-ABC/2 is still widely accepted for volume estimations in spontaneous intracerebral hemorrhage (ICH) despite known limitations, which potentially accounts for controversial outcome-study results. The aim of this study was to establish and validate an automatic segmentation algorithm, allowing for quick and accurate quantification of ICH. Methods-A segmentation algorithm implementing first-and second-order statistics, texture, and threshold features was trained on manual segmentations with a random-forest methodology. Quantitative data of the algorithm, manual segmentations, and ABC/2 were evaluated for agreement in a study sample (n=28) and validated in an independent sample not used for algorithm training (n=30). Results-ABC/2 volumes were significantly larger compared with either manual or algorithm values, whereas no significant differences were found between the latter (P<0.0001; Friedman+Dunn's multiple comparison

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Section: Discussionmentioning

confidence: 99%

Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage

Scherer

Cordes

Younsi

et al. 2016

Stroke

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“…Other computer-aided detection methods have been proposed for intracranial hemorrhages; however, these are not suitable to automatically quantify SAH. For instance, the method of Chan 32 is based on the symmetry of the ventricles, which are segmented by thresholding only. Here, the assumption is made that no blood is present in the ventricles, which is often not the case in patients with SAH.…”

Section: Discussionmentioning

confidence: 99%

Automatic Quantification of Subarachnoid Hemorrhage on Noncontrast CT

Boers

Zijlstra²,

Gathier

et al. 2014

AJNR Am J Neuroradiol

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“…Although some systems [6,7] claimed to be able to detect hemorrhage including SAH, they could detect only a small part of SAH with asymmetry and hyper-intensity. To be able to handle the exclusive features of SAH (partly hyperdense in SAS, and most with symmetry to the brain), we propose to detect the existence of SAH through two steps: approximation of the SAS via registration of an atlas with the patient data, and detecting abnormality in the approximated SAS through support vector machine (SVM) based pattern classification.…”

Section: Introductionmentioning

confidence: 96%

“…As shown in [5], misinterpretation of CT in patients with SAH is a common problem. Various hemorrhage detection systems have been reported [6][7][8][9][10], but none of them are suitable for the detection of SAH. These methods have two common assumptions: the hemorrhagic region in brain CT being hyperdense, and asymmetric.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of the Existence of Subarachnoid Hemorrhage from Clinical CT Images

et al. 2010

J Med Syst

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Subarachnoid hemorrhage (SAH) is a medical emergency which can lead to death or severe disability. Misinterpretation of computed tomography (CT) in patients with SAH is a common problem. How to improve the accuracy of diagnosis is a great challenge to both the clinical physicians and medical researchers. In this paper we proposed a method for the automatic detection of SAH on clinical non-contrast head CT scans. The novelty includes approximation of the subarachnoid space in head CT using an atlas based registration, and exploration of support vector machine to the detection of SAH. The study included 60 patients with SAH and 69 normal controls from clinical hospitals. Thirty patients with SAH and 30 normal controls were used for training, while the rest were used for testing to achieve a testing sensitivity of 100% and specificity of 89.7%. The proposed algorithm might be a potential tool to screen the existence of SAH.

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Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain

Cited by 119 publications

References 12 publications

Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage

Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage

Automatic Quantification of Subarachnoid Hemorrhage on Noncontrast CT

Automatic Detection of the Existence of Subarachnoid Hemorrhage from Clinical CT Images

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