Table of Links
Abstract and 1. Introduction
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Materials and Methods
2.1 Vector Database and Indexing
2.2 Feature Extractors
2.3 Dataset and Pre-processing
2.4 Search and Retrieval
2.5 Re-ranking retrieval and evaluation
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Evaluation and 3.1 Search and Retrieval
3.2 Re-ranking
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Discussion
4.1 Dataset and 4.2 Re-ranking
4.3 Embeddings
4.4 Volume-based, Region-based and Localized Retrieval and 4.5 Localization-ratio
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Conclusion, Acknowledgement, and References
5 Conclusion
Our study establishes a new benchmark for the retrieval of anatomical structures within 3D medical volumes, utilizing the TotalSegmentator dataset to facilitate targeted queries of volumes or sub-volumes for specific anatomical structures. The results highlight the potential of leveraging pre-trained vision embeddings, originally trained on non-medical images, for medical image retrieval across diverse anatomical regions with a wide size range.
We introduced a re-ranking method based on a late interaction model from text retrieval, i.e. ColBERT Khattab and Zaharia [2020]. The proposed ColBERT-inspired method enhances the retrieval recall of all anatomical regions. Future investigations can focus on refining and optimizing the computational efficiency of the proposed re-ranking method.
We evaluated the performance of different embeddings pre-trained supervised and self-supervised on medical and non-medical data. The results indicate that pre-training on general natural images (e.g., ImageNet) yields slightly more effective embedding vectors than domain-specific natural images (e.g., RadImageNet). However, given the marginal difference, the choice of embeddings is unlikely to impact the user experience in downstream tasks significantly.
The retrieval of certain anatomical structures, such as the brain and face, demonstrates low recall across all embedding and retrieval methods. Subsequent research can explore the prevalence of such patterns and potential solutions.
This benchmark sets the stage for future advancements in content-based medical image retrieval, particularly in localizing specific organs or areas within scans.
Acknowledgement
The authors like to thank the Bayer team of the internal ML innovation platform for providing compute infrastructure and technical support.
We thank Timothy Deyer and his RadImageNet team for providing the RadImageNet pre-trained model weights for the SwinTransformer architecture.
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Authors:
(1) Farnaz Khun Jush, Bayer AG, Berlin, Germany (farnaz.khunjush@bayer.com);
(2) Steffen Vogler, Bayer AG, Berlin, Germany (steffen.vogler@bayer.com);
(3) Tuan Truong, Bayer AG, Berlin, Germany (tuan.truong@bayer.com);
(4) Matthias Lenga, Bayer AG, Berlin, Germany (matthias.lenga@bayer.com).
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This paper is available on arxiv under CC BY 4.0 DEED license.
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