Predicting vasospasm risk using first presentation aneurysmal subarachnoid haemorrhage volume: a semi-automated CT image segmentation analysis in ITK-SNAP

Street, James S.; Pandit, Anand; Toma, Ahmed K

doi:10.1101/2023.01.22.23284860

Cited by 3 publications

(4 citation statements)

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“…For example, physicians utilize the Fisher scale to subjectively evaluate the severity of the vasospasm from patients' CT examinations [6]. Another study estimated the total blood volume of aSAH regions based on the acquired CT images, and uses this blood volume as a clinical marker to predict vasospasm [7]. However, these methods are based on visual interpretation or single quantification feature, which may not be able to fully utilize the copious information on CT images.…”

Section: Introductionmentioning

confidence: 99%

Developing a CT-image-based clinical marker to predict vasospasm for aneurysmal subarachnoid hemorrhage patients

Sadri,

Abdoli,

Zhang

et al. 2024

Biophotonics and Immune Responses XIX

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Vasospasm is a common complication in aneurysmal subarachnoid hemorrhage (aSAH). Currently, patients with aSAH are usually monitored at intensive care unit (ICU) for approximately 14 days for early detection and treatment of vasospasm. To facilitate the diagnosis and decision-making process, this investigation aims to combine radiomics and deep learning technologies to predict vasospasm that requires intra-arterial treatment for patients with aSAH. For this purpose, a retrospective dataset was collected, containing a total of 52 aSAH patients. Next, a total of 1032 radiomic features and 768 vision transformer (ViT) based features were computed for each case to comprehensively quantify the aSAH characteristics. Based on the initial feature pool, analysis of variance (ANOVA) F1 score was applied to select 30 best performed features as the optimal feature cluster. Finally, a support vector machine (SVM) based classifier was trained to predict the vasospasm, and the model performance was evaluated using a 4-fold cross-validation strategy. Receiver operating characteristics (ROC) curve and confusion matrix were adopted as the assessing indices. The result show that the model achieved an area under the ROC curve (AUC) of 0.86±0.03, positive predictive value of 78%, negative predictive value of 76%, and overall accuracy of 77%, respectively. This investigation initially verified the feasibility of using CT images to accurately predict cerebral vasospasm.

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Section: Introductionmentioning

confidence: 99%

Developing a CT-image-based clinical marker to predict vasospasm for aneurysmal subarachnoid hemorrhage patients

Sadri,

Abdoli,

Zhang

et al. 2024

Biophotonics and Immune Responses XIX

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“…There are different methods for binary segmentation of subarachnoid hemorrhage including threshold-based (16), semi-automated (17), and deep-learning-based methods (15,19) not only quantifying the SAH-volume but also trying to predict parameters that are relevant to the patient’s outcome – e.g. the prediction of vasospasm risk (17). Whereas a manual segmentation provided by an experienced human rater is considered the gold standard (17), this has several limitations including the high time effort and relatively low interrater reliability (15,16).…”

Section: Introductionmentioning

confidence: 99%

“…the prediction of vasospasm risk (17). Whereas a manual segmentation provided by an experienced human rater is considered the gold standard (17), this has several limitations including the high time effort and relatively low interrater reliability (15,16). Deep learning based methods have the potential to provide a more objective assessment of hemorrhage volumes in an automated way.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several studies have proposed quantifying the SAH volume using automated segmentation methods in NCCT (15)(16)(17)(18). There are different methods for binary segmentation of subarachnoid hemorrhage including threshold-based (16), semi-automated (17), and deep-learning-based methods (15,19) not only quantifying the SAH-volume but also trying to predict parameters that are relevant to the patient's outcome -e.g. the prediction of vasospasm risk (17).…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-based Multiclass Segmentation in Aneurysmal Subarachnoid Hemorrhage

Kiewitz,

Aydin,

Hilbert

et al. 2024

Preprint

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IntroductionAneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition with a significant variability in patients’ outcomes. Radiographic scores used to assess the extent of SAH or other potentially outcome-relevant pathologies are limited by interrater variability and do not utilize all available information from the imaging. Image segmentation plays an important role in extracting relevant information from images by enabling precise identification and delineation of objects or regions of interest. Thus, segmentation offers the potential for automatization of score assessments and downstream outcome prediction using precise volumetric information. Our study aims to develop a deep learning model that enables automated multiclass segmentation of structures and pathologies relevant for aSAH outcome prediction.MethodsOut of 408 patients treated with aSAH in the department of Neurosurgery at Charité University Hospital in Berlin from 2009 to 2015, a subset of 73 representative CT scans were included in our retrospective study. All non-contrast CT scans (NCCT) were manually segmented to create a ground truth. For the multiclass segmentation task we determined six different target classes: basal and cortical SAH, intraventricular hemorrhage (IVH), ventricles, intracerebral hemorrhage (ICH), aneurysms and subdural hematoma (SDH). An additional hemorrhage class was created by merging the individual hemorrhage classes. The set of 73 NCCT was splitted into three stratified sets: training set (43 patients), validation set (10 patients) and test set (20 patients). We used the nnUnet deep learning based biomedical image segmentation tool and implemented 2d and 3d configurations. Additionally, we performed an interrater reliability analysis for multiclass segmentation and assessed the generalizability of the model on an external dataset of primary ICH patients (n=104). Segmentation performance was evaluated using: median Dice coefficient, volumetric similarity and sensitivity. Additionally, a global Dice coefficient was calculated by considering all patients in the test set to be one single concatenated image.ResultsThe nnUnet-based segmentation model demonstrated performance closely matching the interrater reliability observed between two senior human raters for the SAH, ventricles, ICH classes and overall hemorrhage segmentation. For the hemorrhage segmentation a global Dice coefficient of 0.730 was achieved by the 3d model and a global Dice coefficient of 0.736 was achieved by the 2d model. The global Dice coefficient of the SAH class was 0.686 for both of the nnUnet models; ICH: 0.743 (3d model), 0.765 (2d model); ventricles: 0.875 (3d model), 0.872 (2d model). In the IVH, aneurysm and SDH classes the nnUnet models performance differed the most from the human level performance. Overall, the interrater reliability of the SAH class was observed to be lower than in other classes. In the external test set a global Dice coefficient of 0.838 for the hemorrhage segmentation was achieved.ConclusionDeep learning enables automated multiclass segmentation of aSAH-related pathologies and achieves performance approaching that of a human rater. This enables automatized volumetries of pathologies identified on admission CTs in aSAH patients potentially leading to imaging biomarkers for improved aSAH outcome prediction.

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A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation

Jain,

Lee,

Luo

et al. 2024

NeuroSci

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The purpose of the article is to provide a practical guide for manual and semi-automated image segmentation of common neurosurgical cranial lesions, namely meningioma, glioblastoma multiforme (GBM) and subarachnoid haemorrhage (SAH), for neurosurgical trainees and researchers. Materials and Methods: The medical images used were sourced from the Medical Image Computing and Computer Assisted Interventions Society (MICCAI) Multimodal Brain Tumour Segmentation Challenge (BRATS) image database and from the local Picture Archival and Communication System (PACS) record with consent. Image pre-processing was carried out using MRIcron software (v1.0.20190902). ITK-SNAP (v3.8.0) was used in this guideline due to its availability and powerful built-in segmentation tools, although others (Seg3D, Freesurfer and 3D Slicer) are available. Quality control was achieved by employing expert segmenters to review. Results: A pipeline was developed to demonstrate the pre-processing and manual and semi-automated segmentation of patient images for each cranial lesion, accompanied by image guidance and video recordings. Three sample segmentations were generated to illustrate potential challenges. Advice and solutions were provided within both text and video. Conclusions: Semi-automated segmentation methods enhance efficiency, increase reproducibility, and are suitable to be incorporated into future clinical practise. However, manual segmentation remains a highly effective technique in specific circumstances and provides initial training sets for the development of more advanced semi- and fully automated segmentation algorithms.

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Predicting vasospasm risk using first presentation aneurysmal subarachnoid haemorrhage volume: a semi-automated CT image segmentation analysis in ITK-SNAP

Cited by 3 publications

References 65 publications

Developing a CT-image-based clinical marker to predict vasospasm for aneurysmal subarachnoid hemorrhage patients

Developing a CT-image-based clinical marker to predict vasospasm for aneurysmal subarachnoid hemorrhage patients

Deep Learning-based Multiclass Segmentation in Aneurysmal Subarachnoid Hemorrhage

A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation

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