MORE: Multi-Organ Medical Image REconstruction Dataset

• URL: http://arxiv.org/abs/2510.26759v1
• Date: Thu, 30 Oct 2025 17:49:49 GMT
• Title: MORE: Multi-Organ Medical Image REconstruction Dataset
• Authors: Shaokai Wu, Yapan Guo, Yanbiao Ji, Jing Tong, Yuxiang Lu, Mei Li, Suizhi Huang, Yue Ding, Hongtao Lu,
• Abstract summary: We introduce the Multi-Organ medical image REconstruction dataset, comprising CT scans across 9 diverse anatomies with 15 lesion types.<n>This dataset serves two key purposes: (1) enabling robust training of deep learning models on extensive, heterogeneous data, and (2) facilitating rigorous evaluation of model generalization for CT reconstruction.
• Score: 27.136259882514864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: CT reconstruction provides radiologists with images for diagnosis and treatment, yet current deep learning methods are typically limited to specific anatomies and datasets, hindering generalization ability to unseen anatomies and lesions. To address this, we introduce the Multi-Organ medical image REconstruction (MORE) dataset, comprising CT scans across 9 diverse anatomies with 15 lesion types. This dataset serves two key purposes: (1) enabling robust training of deep learning models on extensive, heterogeneous data, and (2) facilitating rigorous evaluation of model generalization for CT reconstruction. We further establish a strong baseline solution that outperforms prior approaches under these challenging conditions. Our results demonstrate that: (1) a comprehensive dataset helps improve the generalization capability of models, and (2) optimization-based methods offer enhanced robustness for unseen anatomies. The MORE dataset is freely accessible under CC-BY-NC 4.0 at our project page https://more-med.github.io/

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