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

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

Image and Video Processing 2024-08-01 v2 Computer Vision and Pattern Recognition

Authors: 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.

Keywords

cancer detection and segmentation breast cancer detection medical image registration

Cite

@article{arxiv.2404.10892,
  title  = {Automatic classification of prostate MR series type using image content and metadata},
  author = {Deepa Krishnaswamy and Bálint Kovács and Stefan Denner and Steve Pieper and David Clunie and Christopher P. Bridge and Tina Kapur and Klaus H. Maier-Hein and Andrey Fedorov},
  journal= {arXiv preprint arXiv:2404.10892},
  year   = {2024}
}

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

Abstract

Keywords

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