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Radiological featur...
Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury
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- MacIntosh, Bradley J. (författare)
- Oslo university hospital,Sunnybrook Health Sciences Centre,University of Toronto
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- Liu, Qinghui (författare)
- Oslo university hospital
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- Schellhorn, Till (författare)
- Oslo university hospital
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- Beyer, Mona K. (författare)
- Oslo university hospital
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- Groote, Inge Rasmus (författare)
- Oslo university hospital,Vestfold Hospital
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- Morberg, Pål C. (författare)
- Oslo university hospital,Vestfold Hospital
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- Poulin, Joshua M. (författare)
- Sunnybrook Health Sciences Centre
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- Selseth, Maiken N. (författare)
- Akershus University Hospital
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- Bakke, Ragnhild C. (författare)
- Oslo university hospital
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- Naqvi, Aina (författare)
- Oslo university hospital
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- Hillal, Amir (författare)
- Skåne University Hospital
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- Ullberg, Teresa (författare)
- Skåne University Hospital
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- Wassélius, Johan (författare)
- Lund University,Lunds universitet,Diagnostisk radiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Neuroradiologi,Forskargrupper vid Lunds universitet,Stroke Imaging Research group,Diagnostic Radiology, (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Neuroradiology,Lund University Research Groups,Skåne University Hospital
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- Rønning, Ole M. (författare)
- University of Oslo,Akershus University Hospital
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- Selnes, Per (författare)
- Akershus University Hospital
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- Kristoffersen, Espen S. (författare)
- Akershus University Hospital,University of Oslo
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- Emblem, Kyrre Eeg (författare)
- Oslo university hospital
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- Skogen, Karoline (författare)
- Oslo university hospital
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- Sandset, Else C. (författare)
- Oslo university hospital
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- Bjørnerud, Atle (författare)
- University of Oslo,Oslo university hospital
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
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Ingår i: Frontiers in Neurology. - 1664-2295. ; 14
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- Introduction: Radiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study. Methods: Non-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases. Results: The hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45–0.74; each p-value < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), resulting in a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (with sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70. Discussion: An open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
Nyckelord
- computed tomography
- deep learning
- intracerebral hemorrhage
- segmentation
- stroke
- traumatic brain injury
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MacIntosh, Bradl ...
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Liu, Qinghui
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Schellhorn, Till
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Beyer, Mona K.
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Groote, Inge Ras ...
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Morberg, Pål C.
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visa fler...
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Poulin, Joshua M ...
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Selseth, Maiken ...
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Bakke, Ragnhild ...
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Naqvi, Aina
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Hillal, Amir
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Ullberg, Teresa
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Wassélius, Johan
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Rønning, Ole M.
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Selnes, Per
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Kristoffersen, E ...
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Emblem, Kyrre Ee ...
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Skogen, Karoline
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Sandset, Else C.
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Bjørnerud, Atle
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