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arXiv · 2404.10892

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

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

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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Deepa Krishnaswamy, Bálint Kovács, Stefan Denner, Steve Pieper, David Clunie, Christopher P. Bridge, Tina Kapur, Klaus H. Maier-Hein, Andrey Fedorov. 2024-04-16. Automatic classification of prostate MR series type using image content and metadata. https://arxiv.org/abs/2404.10892

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