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This is a Quantitative Study study.

October 9, 2025Open Access

Rethink Domain Generalization in Heterogeneous Sequence MRI Segmentation

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Authors

ZZZheyuan ZhangNorthwestern University LPLinkai PengNorthwestern University WDWanying DouNorthwestern University

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Implication

This research presents a dataset and semi-supervised approach targeting pancreas segmentation in MRI, suggesting new insights into domain generalization.

Key Points

The pancreas segmentation task shows that standard methods miss 20-30% of the pancreas in MRI scans, revealing significant challenges.
A new dataset, PancreasDG, includes 563 scans enabling analysis of both cross-center and cross-sequence variations for improved segmentation.
A semi-supervised method leveraging anatomical invariances achieves a 61.63% improvement in Dice scores, outperforming existing generalization techniques.
Insights from the dataset indicate that limited sampling leads to variability misinterpreted as distribution shifts, impacting segmentation accuracy.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68e7f0af2d7e30942762c976 https://doi.org/10.48550/arxiv.2507.23110

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Implication

This research presents a dataset and semi-supervised approach targeting pancreas segmentation in MRI, suggesting new insights into domain generalization.

Rethink Domain Generalization in Heterogeneous Sequence MRI Segmentation

Discussion

Member takes

Implication

Key Points

Cite This Study

Rethink Domain Generalization in Heterogeneous Sequence MRI Segmentation

Discussion

Member takes

Implication

Key Points

Cite This Study

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