MRSegmentator: Robust Multi-Modality Segmentation of 40 Classes in MRI and CT Sequences
CoRR(2024)
摘要
Purpose: To introduce a deep learning model capable of multi-organ
segmentation in MRI scans, offering a solution to the current limitations in
MRI analysis due to challenges in resolution, standardized intensity values,
and variability in sequences.
Materials and Methods: he model was trained on 1,200 manually annotated MRI
scans from the UK Biobank, 221 in-house MRI scans and 1228 CT scans, leveraging
cross-modality transfer learning from CT segmentation models. A
human-in-the-loop annotation workflow was employed to efficiently create
high-quality segmentations. The model's performance was evaluated on NAKO and
the AMOS22 dataset containing 600 and 60 MRI examinations. Dice Similarity
Coefficient (DSC) and Hausdorff Distance (HD) was used to assess segmentation
accuracy. The model will be open sourced.
Results: The model showcased high accuracy in segmenting well-defined organs,
achieving Dice Similarity Coefficient (DSC) scores of 0.97 for the right and
left lungs, and 0.95 for the heart. It also demonstrated robustness in organs
like the liver (DSC: 0.96) and kidneys (DSC: 0.95 left, 0.95 right), which
present more variability. However, segmentation of smaller and complex
structures such as the portal and splenic veins (DSC: 0.54) and adrenal glands
(DSC: 0.65 left, 0.61 right) revealed the need for further model optimization.
Conclusion: The proposed model is a robust, tool for accurate segmentation of
40 anatomical structures in MRI and CT images. By leveraging cross-modality
learning and interactive annotation, the model achieves strong performance and
generalizability across diverse datasets, making it a valuable resource for
researchers and clinicians. It is open source and can be downloaded from
https://github.com/hhaentze/MRSegmentator.
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