Segmentation of Spontaneous Intracerebral Hemorrhage on CT With a Region Growing Method Based on Watershed Preprocessing

[Research on intelligent tooth segmentation method combining multiple seed region growth and boundary extension]

The segmentation of dental models is a crucial step in computer-aided diagnosis and treatment systems for oral healthcare. To address the issues of poor universality and under-segmentation in tooth segmentation techniques, an intelligent tooth segmentation method combining multiple seed region growth and boundary extension is proposed. This method utilized the distribution characteristics of negative curvature meshes in teeth to obtain new seed points and effectively adapted to the structural differences between the top and sides of teeth through differential region growth. Additionally, the boundaries of the initial segmentation were extended based on geometric features, which was effectively compensated for under-segmentation issues in region growth. Ablation experiments and comparative experiments with current state-of-the-art algorithms demonstrated that the proposed method achieved better segmentation of crowded dental models and exhibited strong algorithm universality, thus possessing the capability to meet the practical segmentation needs in oral healthcare.

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis

BACKGROUND

Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking.

MATERIALS AND METHODS

PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively.

RESULTS

A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P =0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P =0.001).

CONCLUSION

This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.

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

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.