Automatic classification of prostate MR series type using image content and metadata

Andrey Fedorov; B\'alint Kov\'acs; Christopher P. Bridge; David Clunie; Deepa Krishnaswamy; Klaus H. Maier-Hein; Stefan Denner; Steve Pieper; Tina Kapur

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Automatic classification of prostate MR series type using image content and metadata

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arxiv 2404.10892 v2 pith:KC4ZJKZG submitted 2024-04-16 eess.IV cs.CV

Deepa Krishnaswamy , Bálint Kovács , Stefan Denner , Steve Pieper , David Clunie , Christopher P. Bridge , Tina Kapur , Klaus H. Maier-Hein

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Andrey Fedorov

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classification eess.IVcs.CV

keywords imagemetadatadataclassificationprostatetypealoneanalysis

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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.

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