ArticleDiagnostics (Basel, Switzerland)2022
Fully Automated Thrombus Segmentation on CT Images of Patients with Acute Ischemic Stroke.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 14 citations in OpenAlex.
- Interventional management of acute mesenteric venous thrombosis: a systematic review of current evidence, emerging techniques and future perspectives.Abdominal radiology (New York) · 2026Review
- Automated collateral assessment restricted to the hypoperfused area for distal vessel occlusions in ischemic stroke.European radiology · 2025Article
- Acute ischemic stroke patient factors associated with poor outcomes in patients with favorable collaterals and successful thrombectomy.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2025Article
- Thrombus perviousness in acute ischemic stroke: a scoping review of methodology, predictive value, and future perspectives.Neuroradiology · 2025Article
- Value of Automatically Derived Full Thrombus Characteristics: An Explorative Study of Their Associations with Outcomes in Ischemic Stroke Patients.Journal of clinical medicine · 2024Article
- What is a Challenging Clot? : A DELPHI Consensus Statement from the CLOTS 7.0 Summit.Clinical neuroradiology · 2023Article
- Thrombus imaging characteristics within acute ischemic stroke: similarities and interdependence.Journal of neurointerventional surgery · 2023Article
- Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model.AJNR. American journal of neuroradiology · 2023Article
- Agreement between estimated computed tomography perfusion ischemic core and follow-up infarct on diffusion-weighted imaging.Insights into imaging · 2022Article
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Authors and funding
25 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thrombus imaging characteristics are associated with treatment success and functional outcomes in stroke patients. However, assessing these characteristics based on manual annotations is labor intensive and subject to observer bias. Therefore, we aimed to create an automated pipeline for consistent and fast full thrombus segmentation. We used multi-center, multi-scanner datasets of anterior circulation stroke patients with baseline NCCT and CTA for training (n = 228) and testing (n = 100). We first found the occlusion location using StrokeViewer LVO and created a bounding box around it. Subsequently, we trained dual modality U-Net based convolutional neural networks (CNNs) to segment the thrombus inside this bounding box. We experimented with: (1) U-Net with two input channels for NCCT and CTA, and U-Nets with two encoders where (2) concatenate, (3) add, and (4) weighted-sum operators were used for feature fusion. Furthermore, we proposed a dynamic bounding box algorithm to adjust the bounding box. The dynamic bounding box algorithm reduces the missed cases but does not improve Dice. The two-encoder U-Net with a weighted-sum feature fusion shows the best performance (surface Dice 0.78, Dice 0.62, and 4% missed cases). Final segmentation results have high spatial accuracies and can therefore be used to determine thrombus characteristics and potentially benefit radiologists in clinical practice.
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