BackgroundThe ABC/2 method is usually applied to evaluate intracerebral hemorrhage (ICH) volume on computed tomography (CT), although it might be inaccurate and not applicable in estimating extradural or subdural hemorrhage (EDH, SDH) volume due to their irregular hematoma shapes. This study aimed to evaluate deep framework optimized for the segmentation and quantification of ICH, EDH, and SDH.MethodsThe training datasets were 3,000 images retrospectively collected from a collaborating hospital (Hospital A) and segmented by the Dense U-Net framework. Three experienced radiologists determined the ground truth by marking the pixels as hemorrhage area. We utilized the Dice and intra-class correlation coefficients (ICC) to test the reliability of the ground truth. Moreover, the testing datasets consisted of 211 images (internal test) from Hospital A, and 86 ICH images (external test) from another hospital (Hospital B). In this study, we chose scatter plots, ICC, and Pearson correlation coefficients (PCC) with ground truth to evaluate the performance of the deep framework. Furthermore, to validate the effectiveness of the deep framework, we did a comparative analysis of the hemorrhage volume estimation between the deep model and the ABC/2 method.ResultsThe high Dice (0.89–0.95) and ICC (0.985–0.997) showed the consistency of the manual segmentations among the radiologists and the reliability of the ground truth. For the internal test, the Dice coefficients of ICH, EDH, and SDH were 0.90 ± 0.06, 0.88 ± 0.12, and 0.82 ± 0.16, respectively. For the external test, the segmentation Dice was 0.86 ± 0.09. Comparatively, the ICC and PCC of ICH volume estimations were 0.99 performed by Dense U-Net that overmatched the ABC/2 method.ConclusionThis study revealed the excellent performance of hematoma segmentation and volume evaluation based on Dense U-Net, which indicated our deep framework might contribute to efficiently developing treatment strategies for intracranial hemorrhage in clinics.
• Haemorrhage risk stratification is important for children with untreated brain AVM. • Angiographic features suggesting unbalanced inflow and outflow predict paediatric brain AVM haemorrhage. • Identifying AVMs with high rupture risk help patient selection and tailoring treatment.
Purpose:To develop a method that combines a fi xed-T1, fuzzy c-means (FCM) technique with a reference region (RR) model (T1-FCM method) to estimate pharmacokinetic parameters without measuring the arterial input function or baseline T1, or T1(0), and to demonstrate its feasibility in the assessment of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer by using data from dynamic contrast material-enhanced magnetic resonance (MR) imaging. Materials and Methods:This study was approved by the human investigation committees of the two participating institutions. All patients gave written informed consent. A conventional dual-fl ipangle gradient-echo method was used to evaluate the effects of noise and the T1 in the tissue itself on the accuracy of T1 estimation. Both conventional RR and fi xed-T1 methods were used to evaluate the effects of noise and preselected T1(0) on the estimation of pharmacokinetic parameters by means of a simulation study. Thirty-three women (age range, 32-66 years; mean age, 45 years) with pathologically proved breast tumors were examined to evaluate the feasibility of using the T1-FCM method as a means of assessing treatment response to NAC. A nonparametric Mann-Whitney U test was used to assess the difference in each of the MR imaging parameters between patients with a major histologic response to treatment and those with a nonmajor histologic response. Results:With use of the dual-fl ip-angle method, the accuracy and distribution of T1 estimation are dependent on the T1 in the tissue itself. The T1-FCM method is more accurate than other methods and is relatively insensitive to the effects of noise and incorrect T1(0) selection. Preliminary clinical data revealed a signifi cant difference ( P , .01) in the change of the volume transfer constant after two cycles of NAC between the major and nonmajor histologic response groups. Conclusion:Results of the simulation study demonstrate that the T1-FCM method appears to be relatively insensitive to noisy dynamic contrast-enhanced MR imaging data. This method could prove useful in the evaluation of breast cancer therapy.q RSNA, 2010
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.