publications

2025

1. 

   GuidedMorph: Two-Stage Deformable Registration for Breast MRI

   Yaqian Chen, Hanxue Gu, Haoyu Dong, and 5 more authors

   arXiv preprint arXiv:2505.13414, 2025

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   This work proposes a two-stage deformable registration method for breast MRI.

   @article{chen2025guidedmorph,
     title = {GuidedMorph: Two-Stage Deformable Registration for Breast MRI},
     author = {Chen, Yaqian and Gu, Hanxue and Dong, Haoyu and Li, Qihang and Chen, Yuwen and Konz, Nicholas and Li, Lin and Mazurowski, Maciej A},
     journal = {arXiv preprint arXiv:2505.13414},
     year = {2025},
     dimensions = {true},
     bibtex_show = true
   }

2. 

   Breast density in MRI: an AI-based quantification and relationship to assessment in mammography

   Yaqian Chen, Lin Li, Hanxue Gu, and 5 more authors

   npj Breast Cancer, 2025

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   This work quantifies breast density in MRI and explores its relationship to mammography assessment.

   @article{chen2025breast,
     title = {Breast density in MRI: an AI-based quantification and relationship to assessment in mammography},
     author = {Chen, Yaqian and Li, Lin and Gu, Hanxue and Dong, Haoyu and Nguyen, Derek L and Kirk, Allan D and Mazurowski, Maciej A and Hwang, E Shelley},
     journal = {npj Breast Cancer},
     volume = {11},
     number = {1},
     pages = {115},
     year = {2025},
     publisher = {Nature Publishing Group UK London},
     dimensions = {true},
     bibtex_show = true
   }

3. 

   Automated muscle and fat segmentation in computed tomography for comprehensive body composition analysis

   Yaqian Chen, Hanxue Gu, Yuwen Chen, and 7 more authors

   arXiv preprint arXiv:2502.09779, 2025

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   This work proposes an automated muscle and fat segmentation method for comprehensive body composition analysis.

   @article{chen2025automated,
     title = {Automated muscle and fat segmentation in computed tomography for comprehensive body composition analysis},
     author = {Chen, Yaqian and Gu, Hanxue and Chen, Yuwen and Yang, Jichen and Dong, Haoyu and Cao, Joseph Y and Camarena, Adrian and Mantyh, Christopher and Colglazier, Roy and Mazurowski, Maciej A},
     journal = {arXiv preprint arXiv:2502.09779},
     year = {2025},
     dimensions = {true},
     bibtex_show = true
   }

4. 

   Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2

   Yuwen Chen, Zafer Yildiz, Qihang Li, and 5 more authors

   arXiv preprint arXiv:2505.01854, 2025

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   This work proposes a method for accelerating volumetric medical image annotation via short-long memory SAM 2.

   @article{chen2025accelerating,
     title = {Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2},
     author = {Chen, Yuwen and Yildiz, Zafer and Li, Qihang and Chen, Yaqian and Dong, Haoyu and Gu, Hanxue and Konz, Nicholas and Mazurowski, Maciej A},
     journal = {arXiv preprint arXiv:2505.01854},
     year = {2025},
     dimensions = {true},
     bibtex_show = true
   }

5. 

   Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?

   Hanxue Gu, Yaqian Chen, Nicholas Konz, and 2 more authors

   In Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, 2025

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   This work evaluates the readiness of vision foundation models for out-of-the-box medical image registration.

   @inproceedings{gu2025vision,
     title = {Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?},
     author = {Gu, Hanxue and Chen, Yaqian and Konz, Nicholas and Li, Qihang and Mazurowski, Maciej A},
     booktitle = {Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care},
     pages = {101--112},
     year = {2025},
     organization = {Springer},
     dimensions = {true},
     bibtex_show = true
   }

6. 

   Segmentanybone: A universal model that segments any bone at any location on mri

   Hanxue Gu, Roy Colglazier, Haoyu Dong, and 8 more authors

   Medical Image Analysis, 2025

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   This work provides a foundation model to segment bones across any location.

   @article{gu2025segmentanybone,
     title = {Segmentanybone: A universal model that segments any bone at any location on mri},
     author = {Gu, Hanxue and Colglazier, Roy and Dong, Haoyu and Zhang, Jikai and Chen, Yaqian and Yildiz, Zafer and Chen, Yuwen and Li, Lin and Yang, Jichen and Willhite, Jay and others},
     journal = {Medical Image Analysis},
     pages = {103469},
     year = {2025},
     publisher = {Elsevier},
     dimensions = {true},
     bibtex_show = true,
   }

7. 

   MRI-CORE: A Foundation Model for Magnetic Resonance Imaging

   Haoyu Dong, Yuwen Chen, Hanxue Gu, and 4 more authors

   arXiv preprint arXiv:2506.12186, 2025

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   This work introduces MRI-CORE, a foundation model for magnetic resonance imaging.

   @article{dong2025mri,
     title = {MRI-CORE: A Foundation Model for Magnetic Resonance Imaging},
     author = {Dong, Haoyu and Chen, Yuwen and Gu, Hanxue and Konz, Nicholas and Chen, Yaqian and Li, Qihang and Mazurowski, Maciej A},
     journal = {arXiv preprint arXiv:2506.12186},
     year = {2025},
     dimensions = {true},
     bibtex_show = true
   }

8. 

   Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery

   Hanxue Gu, Yaqian Chen, Diego Schaps, and 5 more authors

   arXiv preprint arXiv:2506.11996, 2025

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   We demonstrate that integrating automated body composition metrics into clinical models significantly improves risk prediction for colectomy surgery.

   @article{gu2025improving,
     title = {Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery},
     author = {Gu, Hanxue and Chen, Yaqian and Schaps, Diego and Woody, Regina and Colglazier, Roy and Mazurowski, Maciej A and Mantyh, Christopher and others},
     journal = {arXiv preprint arXiv:2506.11996},
     year = {2025},
     dimensions = {true},
     bibtex_show = true
   }

9. 

   BreastSegNet: Multi-label Segmentation of Breast MRI

   Qihang Li, Jichen Yang, Yaqian Chen, and 4 more authors

   In Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, 2025

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   This work proposes a multi-label segmentation method for breast MRI.

   @inproceedings{li2025breastsegnet,
     title = {BreastSegNet: Multi-label Segmentation of Breast MRI},
     author = {Li, Qihang and Yang, Jichen and Chen, Yaqian and Chen, Yuwen and Gu, Hanxue and Grimm, Lars J and Mazurowski, Maciej A},
     booktitle = {Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care},
     pages = {196--205},
     year = {2025},
     organization = {Springer},
     dimensions = {true},
     bibtex_show = true
   }

10. 

    SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI

    Roy Colglazier, Jisoo Lee, Haoyu Dong, and 8 more authors

    arXiv preprint arXiv:2506.22467, 2025

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    This work proposes a universal muscle segmentation model across different locations in MRI.

    @article{colglazier2025segmentanymuscle,
      title = {SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI},
      author = {Colglazier, Roy and Lee, Jisoo and Dong, Haoyu and Gu, Hanxue and Chen, Yaqian and Cao, Joseph and Yildiz, Zafer and Liu, Zhonghao and Konz, Nicholas and Yang, Jichen and others},
      journal = {arXiv preprint arXiv:2506.22467},
      year = {2025},
      dimensions = {true},
      bibtex_show = true
    }

2024

1. 

   Contourdiff: Unpaired image translation with contourguided diffusion models

   Yuwen Chen, Nicholas Konz, Hanxue Gu, and 5 more authors

   arXiv preprint arXiv:2403.10786, 2024

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   This work proposes a contour-guided diffusion model for unpaired image translation.

   @article{chen2024contourdiff,
     title = {Contourdiff: Unpaired image translation with contourguided diffusion models},
     author = {Chen, Yuwen and Konz, Nicholas and Gu, Hanxue and Dong, Haoyu and Chen, Yaqian and Li, Lin and Lee, Jisoo and Mazurowski, Maciej A},
     journal = {arXiv preprint arXiv:2403.10786},
     volume = {2},
     number = {3},
     pages = {4},
     year = {2024},
     dimensions = {true},
     bibtex_show = true
   }

2. 

   Realtime robust shape estimation of deformable linear object

   Jiaming Zhang, Zhaomeng Zhang, Yihao Liu, and 3 more authors

   In 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024

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   This work proposes a real-time robust shape estimation method for deformable linear objects.

   @inproceedings{zhang2024realtime,
     title = {Realtime robust shape estimation of deformable linear object},
     author = {Zhang, Jiaming and Zhang, Zhaomeng and Liu, Yihao and Chen, Yaqian and Kheradmand, Amir and Armand, Mehran},
     booktitle = {2024 IEEE International Conference on Robotics and Automation (ICRA)},
     pages = {10734--10740},
     year = {2024},
     organization = {IEEE},
     dimensions = {true},
     bibtex_show = true
   }

3. 

   RaD: A Metric for Medical Image Distribution Comparison in Out-of-Domain Detection and Other Applications

   Nicholas Konz, Yuwen Chen, Hanxue Gu, and 3 more authors

   arXiv e-prints, 2024

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   This work introduces RaD, a metric for medical image distribution comparison in out-of-domain detection and other applications.

   @article{konz2024rad,
     title = {RaD: A Metric for Medical Image Distribution Comparison in Out-of-Domain Detection and Other Applications},
     author = {Konz, Nicholas and Chen, Yuwen and Gu, Hanxue and Dong, Haoyu and Chen, Yaqian and Mazurowski, Maciej A},
     journal = {arXiv e-prints},
     pages = {arXiv--2412},
     year = {2024},
     dimensions = {true},
     bibtex_show = true
   }

4. 

   Segment Anything Model 2: An Application to 2D and 3D Medical Images

   Haoyu Dong^*, Hanxue Gu^*, Yaqian Chen, and 3 more authors

   arXiv preprint arXiv:2408.00756, 2024

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   We extend the Segment Anything Model (SAM2) to handle both 2D and 3D medical imaging tasks, demonstrating significant improvements in segmentation flexibility and performance across diverse datasets.

   @article{dong2024segment,
     title = {Segment Anything Model 2: An Application to 2D and 3D Medical Images},
     author = {Dong, Haoyu and Gu, Hanxue and Chen, Yaqian and Yang, Jichen and Chen, Yuwen and Mazurowski, Maciej A},
     journal = {arXiv preprint arXiv:2408.00756},
     year = {2024},
     dimensions = {true},
     bibtex_show = true
   }

2021

1. 

   Monocular object SLAM using quadrics and landmark reference map for outdoor UAV applications

   Pengtao Shao, Fan Mo, Yaqian Chen, and 2 more authors

   In 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2021

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   This work proposes a monocular object SLAM system for outdoor UAV applications using quadrics and landmark reference map.

   @inproceedings{shao2021monocular,
     title = {Monocular object SLAM using quadrics and landmark reference map for outdoor UAV applications},
     author = {Shao, Pengtao and Mo, Fan and Chen, Yaqian and Ding, Ning and Huang, Rui},
     booktitle = {2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)},
     pages = {1195--1201},
     year = {2021},
     organization = {IEEE},
     dimensions = {true},
     bibtex_show = true
   }