Automatic classification of prostate MR series type using image content and metadata
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917737858596864 |
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| author | Krishnaswamy, Deepa Kovács, Bálint Denner, Stefan Pieper, Steve Clunie, David Bridge, Christopher P. Kapur, Tina Maier-Hein, Klaus H. Fedorov, Andrey |
| author_facet | Krishnaswamy, Deepa Kovács, Bálint Denner, Stefan Pieper, Steve Clunie, David Bridge, Christopher P. Kapur, Tina Maier-Hein, Klaus H. Fedorov, Andrey |
| contents | With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial intelligence-based analysis. However, incomplete or missing metadata prevents effective automation. We therefore propose a deep-learning method for classification of prostate cancer scanning sequences based on a combination of image data and DICOM metadata. We demonstrate superior results compared to metadata or image data alone, and make our code publicly available at https://github.com/deepakri201/DICOMScanClassification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10892 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic classification of prostate MR series type using image content and metadata Krishnaswamy, Deepa Kovács, Bálint Denner, Stefan Pieper, Steve Clunie, David Bridge, Christopher P. Kapur, Tina Maier-Hein, Klaus H. Fedorov, Andrey Image and Video Processing Computer Vision and Pattern Recognition With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial intelligence-based analysis. However, incomplete or missing metadata prevents effective automation. We therefore propose a deep-learning method for classification of prostate cancer scanning sequences based on a combination of image data and DICOM metadata. We demonstrate superior results compared to metadata or image data alone, and make our code publicly available at https://github.com/deepakri201/DICOMScanClassification. |
| title | Automatic classification of prostate MR series type using image content and metadata |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.10892 |