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Research ArticleORIGINAL RESEARCH

Automated Detection of Black Hole Sign for Intracerebral Hemorrhage Patients Using Self-Supervised Learning

Hanyin Wang, Tim Schwirtlich, Ethan J. Houskamp, Meghan R. Hutch, Julianne X. Murphy, Juliana S. do Nascimento, Andrea Zini, Laura Brancaleoni, Sebastiano Giacomozzi, Yuan Luo and Andrew M. Naidech
American Journal of Neuroradiology May 2025, ajnr.A8826; DOI: https://doi.org/10.3174/ajnr.A8826
Hanyin Wang
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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Tim Schwirtlich
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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Ethan J. Houskamp
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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Meghan R. Hutch
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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Julianne X. Murphy
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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Juliana S. do Nascimento
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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Andrea Zini
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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Laura Brancaleoni
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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Sebastiano Giacomozzi
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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Yuan Luo
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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Andrew M. Naidech
From the Department of Preventive Medicine (H.W., T.S., M.R.H, J.X.M, J.S.N, Y.L.), Department of Neurological Surgery (E.J.H.), and Department of Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; IRCCS Istituto delle Scienze Neurologiche di Bologna, Department of Neurology and Stroke Center (A.Z., L.B.), Maggiore Hospital, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM) (S.G.), Alma Mater Studiorum-University of Bologna, Bologna, Italy.
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ABSTRACT

BACKGROUND AND PURPOSE: Intracerebral Hemorrhage (ICH) is a devastating form of stroke. Hematoma expansion (HE), growth of the hematoma on interval scans, predicts death and disability. Accurate prediction of HE is crucial for targeted interventions to improve patient outcomes. The black hole sign (BHS) on non-contrast computed tomography (CT) scans is a predictive marker for HE. An automated method to recognize the BHS and predict HE could speed precise patient selection for treatment.

MATERIALS AND METHODS: In. this paper, we presented a novel framework leveraging self-supervised learning (SSL) techniques for BHS identification on head CT images. A ResNet-50 encoder model was pre-trained on over 1.7 million unlabeled head CT images. Layers for binary classification were added on top of the pre-trained model. The resulting model was fine-tuned using the training data and evaluated on the held-out test set to collect AUC and F1 scores. The evaluations were performed on scan and slice levels. We ran different panels, one using two multi-center datasets for external validation and one including parts of them in the pre-training

RESULTS: Our model demonstrated strong performance in identifying BHS when compared with the baseline model. Specifically, the model achieved scan-level AUC scores between 0.75-0.89 and F1 scores between 0.60-0.70. Furthermore, it exhibited robustness and generalizability across an external dataset, achieving a scan-level AUC score of up to 0.85 and an F1 score of up to 0.60, while it performed less well on another dataset with more heterogeneous samples. The negative effects could be mitigated after including parts of the external datasets in the fine-tuning process.

CONCLUSIONS: This study introduced a novel framework integrating SSL into medical image classification, particularly on BHS identification from head CT scans. The resulting pre-trained head CT encoder model showed potential to minimize manual annotation, which would significantly reduce labor, time, and costs. After fine-tuning, the framework demonstrated promising performance for a specific downstream task, identifying the BHS to predict HE, upon comprehensive evaluation on diverse datasets. This approach holds promise for enhancing medical image analysis, particularly in scenarios with limited data availability.

ABBREVIATIONS: ICH = Intracerebral Hemorrhage; HE = Hematoma Expansion; BHS = Black Hole Sign; CT = Computed Tomography; SSL = Self-supervised Learning; AUC = Area Under the receiver operator Curve; CNN = Convolutional Neural Network; SimCLR = Simple framework for Contrastive Learning of visual Representation; HU = Hounsfield Unit; CLAIM = Checklist for Artificial Intelligence in Medical Imaging; VNA = Vendor Neutral Archive; DICOM = Digital Imaging and Communications in Medicine; NIfTI = Neuroimaging Informatics Technology Initiative; INR = International Normalized Ratio; GPU= Graphics Processing Unit; NIH= National Institutes of Health.

Footnotes

  • The authors declare no conflicts of interest related to the content of this article.

  • © 2025 by American Journal of Neuroradiology

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Cite this article
Accepted Manuscript
Hanyin Wang, Tim Schwirtlich, Ethan J. Houskamp, Meghan R. Hutch, Julianne X. Murphy, Juliana S. do Nascimento, Andrea Zini, Laura Brancaleoni, Sebastiano Giacomozzi, Yuan Luo, Andrew M. Naidech
Automated Detection of Black Hole Sign for Intracerebral Hemorrhage Patients Using Self-Supervised Learning
American Journal of Neuroradiology May 2025, ajnr.A8826; DOI: 10.3174/ajnr.A8826

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Accepted Manuscript
Automated Detection of Black Hole Sign for Intracerebral Hemorrhage Patients Using Self-Supervised Learning
Hanyin Wang, Tim Schwirtlich, Ethan J. Houskamp, Meghan R. Hutch, Julianne X. Murphy, Juliana S. do Nascimento, Andrea Zini, Laura Brancaleoni, Sebastiano Giacomozzi, Yuan Luo, Andrew M. Naidech
American Journal of Neuroradiology May 2025, ajnr.A8826; DOI: 10.3174/ajnr.A8826
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