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Fully automated sinogram-based deep learning model for detection and classification of intracranial hemorrhage.
- Source :
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Medical physics [Med Phys] 2024 Mar; Vol. 51 (3), pp. 1944-1956. Date of Electronic Publication: 2023 Sep 13. - Publication Year :
- 2024
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Abstract
- Purpose: To propose an automated approach for detecting and classifying Intracranial Hemorrhages (ICH) directly from sinograms using a deep learning framework. This method is proposed to overcome the limitations of the conventional diagnosis by eliminating the time-consuming reconstruction step and minimizing the potential noise and artifacts that can occur during the Computed Tomography (CT) reconstruction process.<br />Methods: This study proposes a two-stage automated approach for detecting and classifying ICH from sinograms using a deep learning framework. The first stage of the framework is Intensity Transformed Sinogram Sythesizer, which synthesizes sinograms that are equivalent to the intensity-transformed CT images. The second stage comprises of a cascaded Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model that detects and classifies hemorrhages from the synthesized sinograms. The CNN module extracts high-level features from each input sinogram, while the RNN module provides spatial correlation of the neighborhood regions in the sinograms. The proposed method was evaluated on a publicly available RSNA dataset consisting of a large sample size of 8652 patients.<br />Results: The results showed that the proposed method had a notable improvement as high as 27% in patient-wise accuracies when compared to state-of-the-art methods like ResNext-101, Inception-v3 and Vision Transformer. Furthermore, the sinogram-based approach was found to be more robust to noise and offset errors in comparison to CT image-based approaches. The proposed model was also subjected to a multi-label classification analysis to determine the hemorrhage type from a given sinogram. The learning patterns of the proposed model were also examined for explainability using the activation maps.<br />Conclusion: The proposed sinogram-based approach can provide an accurate and efficient diagnosis of ICH without the need for the time-consuming reconstruction step and can potentially overcome the limitations of CT image-based approaches. The results show promising outcomes for the use of sinogram-based approaches in detecting hemorrhages, and further research can explore the potential of this approach in clinical settings.<br /> (© 2023 American Association of Physicists in Medicine.)
Details
- Language :
- English
- ISSN :
- 2473-4209
- Volume :
- 51
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Medical physics
- Publication Type :
- Academic Journal
- Accession number :
- 37702932
- Full Text :
- https://doi.org/10.1002/mp.16714