Evaluation of Siemens Healthineers’ StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke
PurposeThis study aimed to evaluate the perfomance of Siemens Healthineers’ StrokeSegApp performance in automatically segmenting diffusion and perfusion lesions in patients with acute ischemic stroke and to assess its clinical utility in guiding mechanical thrombectomy decisions.MethodsThis retrospe...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1518477/full |
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| author | Lynnet-Samuel J. Teichmann Ahmed A. Khalil Kersten Villringer Jochen B. Fiebach Stefan Huwer Eli Gibson Ivana Galinovic |
| author_facet | Lynnet-Samuel J. Teichmann Ahmed A. Khalil Kersten Villringer Jochen B. Fiebach Stefan Huwer Eli Gibson Ivana Galinovic |
| author_sort | Lynnet-Samuel J. Teichmann |
| collection | DOAJ |
| description | PurposeThis study aimed to evaluate the perfomance of Siemens Healthineers’ StrokeSegApp performance in automatically segmenting diffusion and perfusion lesions in patients with acute ischemic stroke and to assess its clinical utility in guiding mechanical thrombectomy decisions.MethodsThis retrospective study used MRI data of acute ischemic stroke patients from the prospective observational single-center 1000Plus study, acquired between September 2008 and June 2013 (clinicaltrials.org; NCT00715533) and manually segmented by radiologists as the ground truth. The performance of the StrokeSegApp was compared against this ground truth using the dice similarity coefficient (DSC) and Bland–Altman plots. The study also evaluated the application’s ability to recommend mechanical thrombectomy based on DEFUSE 2 and 3 trial criteria.ResultsThe StrokeSegApp demonstrated a mean DSC of 0.60 (95% CI: 0.57–0.63; n = 241) for diffusion deficit segmentation and 0.80 (95% CI: 0.76–0.85; n = 56) for perfusion deficit segmentation. The mean volume deviation was 0.49 mL for diffusion lesions and −7.69 mL for perfusion lesions. Out of 56 subjects meeting DEFUSE 2/3 criteria in the cohort, it correctly identified mechanical thrombectomy candidates with a sensitivity of 82.1% (95% CI: 63.1–93.9%) and a specificity of 96.4% (95% CI: 81.7–99.9%).ConclusionThe Siemens Healthineers’ StrokeSegApp provides accurate automated segmentation of ischemic stroke lesions, comparable to human experts as well as similar commercial software, and shows potential as a reliable tool in clinical decision-making for stroke treatment. |
| format | Article |
| id | doaj-art-1b5bd6df7eb94fd494d242be6c1a4352 |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-1b5bd6df7eb94fd494d242be6c1a43522025-01-24T16:51:56ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-01-011610.3389/fneur.2025.15184771518477Evaluation of Siemens Healthineers’ StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic strokeLynnet-Samuel J. Teichmann0Ahmed A. Khalil1Kersten Villringer2Jochen B. Fiebach3Stefan Huwer4Eli Gibson5Ivana Galinovic6Center for Stroke Research Berlin, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, GermanyCenter for Stroke Research Berlin, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, GermanyCenter for Stroke Research Berlin, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, GermanyCenter for Stroke Research Berlin, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, GermanySiemens Healthineers AG, Erlangen, GermanySiemens Healthineers AG, Erlangen, GermanyCenter for Stroke Research Berlin, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, GermanyPurposeThis study aimed to evaluate the perfomance of Siemens Healthineers’ StrokeSegApp performance in automatically segmenting diffusion and perfusion lesions in patients with acute ischemic stroke and to assess its clinical utility in guiding mechanical thrombectomy decisions.MethodsThis retrospective study used MRI data of acute ischemic stroke patients from the prospective observational single-center 1000Plus study, acquired between September 2008 and June 2013 (clinicaltrials.org; NCT00715533) and manually segmented by radiologists as the ground truth. The performance of the StrokeSegApp was compared against this ground truth using the dice similarity coefficient (DSC) and Bland–Altman plots. The study also evaluated the application’s ability to recommend mechanical thrombectomy based on DEFUSE 2 and 3 trial criteria.ResultsThe StrokeSegApp demonstrated a mean DSC of 0.60 (95% CI: 0.57–0.63; n = 241) for diffusion deficit segmentation and 0.80 (95% CI: 0.76–0.85; n = 56) for perfusion deficit segmentation. The mean volume deviation was 0.49 mL for diffusion lesions and −7.69 mL for perfusion lesions. Out of 56 subjects meeting DEFUSE 2/3 criteria in the cohort, it correctly identified mechanical thrombectomy candidates with a sensitivity of 82.1% (95% CI: 63.1–93.9%) and a specificity of 96.4% (95% CI: 81.7–99.9%).ConclusionThe Siemens Healthineers’ StrokeSegApp provides accurate automated segmentation of ischemic stroke lesions, comparable to human experts as well as similar commercial software, and shows potential as a reliable tool in clinical decision-making for stroke treatment.https://www.frontiersin.org/articles/10.3389/fneur.2025.1518477/fullacute ischemic strokeautomated lesion segmentationMRI analysis softwarediffusion-weighted imagingperfusion-weighted imagingSiemens Healthineers StrokeSegApp |
| spellingShingle | Lynnet-Samuel J. Teichmann Ahmed A. Khalil Kersten Villringer Jochen B. Fiebach Stefan Huwer Eli Gibson Ivana Galinovic Evaluation of Siemens Healthineers’ StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke Frontiers in Neurology acute ischemic stroke automated lesion segmentation MRI analysis software diffusion-weighted imaging perfusion-weighted imaging Siemens Healthineers StrokeSegApp |
| title | Evaluation of Siemens Healthineers’ StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke |
| title_full | Evaluation of Siemens Healthineers’ StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke |
| title_fullStr | Evaluation of Siemens Healthineers’ StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke |
| title_full_unstemmed | Evaluation of Siemens Healthineers’ StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke |
| title_short | Evaluation of Siemens Healthineers’ StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke |
| title_sort | evaluation of siemens healthineers strokesegapp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke |
| topic | acute ischemic stroke automated lesion segmentation MRI analysis software diffusion-weighted imaging perfusion-weighted imaging Siemens Healthineers StrokeSegApp |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1518477/full |
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