publications

2025

1. 

   How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model

   Hanxue Gu, Haoyu Dong, Jichen Yang, and 1 more author

   Machine Learning for Biomedical Imaging, May 2025

   Abs DOI Bib Code

   This work explores 18 strategies to fine-tune the Segment Anything Model (SAM) on medical imaging datasets.

   @article{Gu_2025,
     title = {How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model},
     volume = {3},
     issn = {2766-905X},
     url = {http://dx.doi.org/10.59275/j.melba.2025-86a6},
     doi = {10.59275/j.melba.2025-86a6},
     number = {May 2025},
     journal = {Machine Learning for Biomedical Imaging},
     publisher = {Machine Learning for Biomedical Imaging},
     author = {Gu, Hanxue and Dong, Haoyu and Yang, Jichen and Mazurowski, Maciej A.},
     year = {2025},
     month = may,
     pages = {88–120},
     dimensions = {true},
     bibtex_show = true,
   }

2. 

   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, May 2025

   Abs Bib Code

   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,
   }

3. 

   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, May 2025

   Abs Bib

   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
   }

2024

1. 

   SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs

   Hanxue Gu, Hongyu He, Roy Colglazier, and 3 more authors

   In Medical Imaging with Deep Learning, May 2024

   Abs Bib

   We propose SuperMask, an automated method for generating multi-label high-resolution segmentations from low-resolution unaligned breast MRIs.

   @inproceedings{gu2024supermask,
     title = {SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs},
     author = {Gu, Hanxue and He, Hongyu and Colglazier, Roy and Axelrod, Jordan and French, Robert and Mazurowski, Maciej A},
     booktitle = {Medical Imaging with Deep Learning},
     pages = {119--133},
     year = {2024},
     organization = {PMLR},
     dimensions = {true},
     bibtex_show = true
   }

2. 

   Predicting 3D Forearm Fracture Angle from Biplanar X-ray Images with Rotational Bone Pose Estimation

   Hanxue Gu, Roy Colglazier, Jikai Zhang, and 3 more authors

   In Medical Imaging with Deep Learning, May 2024

   Abs Bib

   We present a method for estimating 3D forearm fracture angles from standard biplanar X-rays, utilizing rotational bone pose estimation to improve clinical assessment.

   @inproceedings{gu2024predicting,
     title = {Predicting 3D Forearm Fracture Angle from Biplanar X-ray Images with Rotational Bone Pose Estimation},
     author = {Gu, Hanxue and Colglazier, Roy and Zhang, Jikai and Lark, Robert and Alman, Benjamin and Mazurowski, Maciej A},
     booktitle = {Medical Imaging with Deep Learning},
     year = {2024},
     dimensions = {true},
     bibtex_show = true
   }

3. 

   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, May 2024

   Abs Bib

   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
   }

4. 

   A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI

   Christopher O Lew, Majid Harouni, Ella R Kirksey, and 7 more authors

   Scientific Reports, May 2024

   Abs Bib Code

   This work introduces a publicly available deep learning model and dataset for segmenting breast, fibroglandular tissue, and vessels in breast MRI, facilitating research in breast imaging and body composition.

   @article{lew2024publicly,
     title = {A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI},
     author = {Lew, Christopher O and Harouni, Majid and Kirksey, Ella R and Kang, Elianne J and Dong, Haoyu and Gu, Hanxue and Grimm, Lars J and Walsh, Ruth and Lowell, Dorothy A and Mazurowski, Maciej A},
     journal = {Scientific Reports},
     volume = {14},
     number = {1},
     pages = {5383},
     year = {2024},
     publisher = {Nature Publishing Group UK London},
     dimensions = {true},
     bibtex_show = true,
   }

5. 

   Touchstone benchmark: Are we on the right way for evaluating ai algorithms for medical segmentation?

   Pedro RAS Bassi, Wenxuan Li, Yucheng Tang, and 8 more authors

   Advances in Neural Information Processing Systems, May 2024

   Bib

   @article{bassi2024touchstone,
     title = {Touchstone benchmark: Are we on the right way for evaluating ai algorithms for medical segmentation?},
     author = {Bassi, Pedro RAS and Li, Wenxuan and Tang, Yucheng and Isensee, Fabian and Wang, Zifu and Chen, Jieneng and Chou, Yu-Cheng and Kirchhoff, Yannick and Rokuss, Maximilian R and Huang, Ziyan and others},
     journal = {Advances in Neural Information Processing Systems},
     volume = {37},
     pages = {15184--15201},
     dimensions = {true},
     bibtex_show = true,
     year = {2024}
   }

6. 

   Medical image segmentation with intent: Integrated entropy weighting for single image test-time adaptation

   Haoyu Dong, Nicholas Konz, Hanxue Gu, and 1 more author

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, May 2024

   Bib

   @inproceedings{dong2024medical,
     title = {Medical image segmentation with intent: Integrated entropy weighting for single image test-time adaptation},
     author = {Dong, Haoyu and Konz, Nicholas and Gu, Hanxue and Mazurowski, Maciej A},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
     pages = {5046--5055},
     year = {2024},
     dimensions = {true},
     bibtex_show = true,
   }

2023

1. 

   Segment Anything Model for Medical Image Analysis: An Experimental Study

   Maciej A Mazurowski, Haoyu Dong, Hanxue Gu, and 3 more authors

   Medical Image Analysis, May 2023

   Abs Bib

   We experimentally evaluate the Segment Anything Model (SAM) for medical image analysis tasks, highlighting its strengths and limitations across multiple use cases.

   @article{mazurowski2023segment,
     title = {Segment Anything Model for Medical Image Analysis: An Experimental Study},
     author = {Mazurowski, Maciej A and Dong, Haoyu and Gu, Hanxue and Yang, Jichen and Konz, Nicholas and Zhang, Yixin},
     journal = {Medical Image Analysis},
     volume = {89},
     pages = {102918},
     year = {2023},
     dimensions = {true},
     bibtex_show = true
   }

2. 

   FDU-net: deep learning-based three-dimensional diffuse optical image reconstruction

   Bin Deng^*, Hanxue Gu^*, Hongmin Zhu, and 5 more authors

   IEEE transactions on medical imaging, May 2023

   Abs Bib

   This work reconstruct diffuse optical image from signals.

   @article{deng2023fdu,
     title = {FDU-net: deep learning-based three-dimensional diffuse optical image reconstruction},
     author = {Deng, Bin and Gu, Hanxue and Zhu, Hongmin and Chang, Ken and Hoebel, Katharina V and Patel, Jay B and Kalpathy-Cramer, Jayashree and Carp, Stefan A},
     journal = {IEEE transactions on medical imaging},
     volume = {42},
     number = {8},
     pages = {2439--2450},
     year = {2023},
     publisher = {IEEE},
     dimensions = {true},
     bibtex_show = true
   }

2022

1. 

   The intrinsic manifolds of radiological images and their role in deep learning

   Nicholas Konz, Hanxue Gu, Haoyu Dong, and 1 more author

   In International Conference on Medical Image Computing and Computer-Assisted Intervention, May 2022

   Abs Bib

   We compare the intrinsic manifold structure of radiological and natural images, finding that although radiological images have lower intrinsic dimensionality, their features are harder to learn, which helps explain why applying deep learning to medical images remains more challenging than to natural image datasets.

   @inproceedings{konz2022intrinsic,
     title = {The intrinsic manifolds of radiological images and their role in deep learning},
     author = {Konz, Nicholas and Gu, Hanxue and Dong, Haoyu and Mazurowski, Maciej A},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     pages = {684--694},
     year = {2022},
     bibtex_show = true,
     dimensions = {true},
     organization = {Springer Nature Switzerland Cham}
   }