Fengming Lin

Christabel Pankhurst Institute, Oxford Road, University of Manchester, M13 9PL Manchester

I am Fengming Lin (林枫茗), a Postdoctoral Research Associate at the University of Manchester, working in the field of Computational Medicine and Generative Artificial Intelligence.

I received my PhD in Computer Science from the University of Leeds, supervised by Prof. Alejandro F. Frangi, Prof Yan Xia, and Dr Nishant Ravikumar. My doctoral research focused on artificial intelligence for vascular segmentation and modality-generalized aneurysm detection. Prior to that, I obtained my Master’s and Bachelor’s degrees from Shandong University, where I was supervised by Prof. Ju Liu and Prof. Qiang Wu, focusing on deep learning for medical image analysis.

My research interests lie at the intersection of AI, medical image analysis, and computational modeling, with applications to:

Image segmentation, registration, and generative modeling
Digital twin and virtual population construction
In-silico trials for cerebral vascular and cardiovascular devices
Multi-modal and domain-generalizable deep learning methods

I have published 17 papers in international journals and conferences such as Computer Methods and Programs in Biomedicine (CMPB) and IEEE ISBI on Google Scholar.

I am actively involved in international collaborations on fluid dynamics modeling, generative models, and large language models for medical applications. Beyond research, I also serve as a reviewer for leading journals such as Medical Image Analysis, IEEE TMI, IEEE TNNLS, IEEE TAI, and PR.

My long-term goal is to advance the development of AI-driven digital twins and in-silico clinical trials, bridging computational science and healthcare innovation.

news

Jun 16, 2025	Website launch announcement: the new website is now live!

selected publications

Lin, F. , Zakeri, Arezoo, Xue, Yidan, MacRaild, Michael, Dou, Haoran, and 5 more authors. (2025). From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction. arXiv preprint arXiv:2505.03599.

preprints Bib

@article{lin2025pixels,
  title = {From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction},
  author = {Lin, Fengming and Zakeri, Arezoo and Xue, Yidan and MacRaild, Michael and Dou, Haoran and Zhou, Zherui and Zou, Ziwei and Sarrami-Foroushani, Ali and Duan, Jinming and Frangi, Alejandro F},
  journal = {arXiv preprint arXiv:2505.03599},
  year = {2025},
}

Lin, F. , Xia, Yan, Song, Shuang, Ravikumar, Nishant, and Frangi, Alejandro F (2023). High-throughput 3DRA segmentation of brain vasculature and aneurysms using deep learning. Computer Methods and Programs in Biomedicine, 230:107355.

journals Bib

@article{lin2023high,
  title = {High-throughput 3DRA segmentation of brain vasculature and aneurysms using deep learning},
  author = {Lin, Fengming and Xia, Yan and Song, Shuang and Ravikumar, Nishant and Frangi, Alejandro F},
  journal = {Computer Methods and Programs in Biomedicine},
  volume = {230},
  pages = {107355},
  year = {2023},
  publisher = {Elsevier},
}

Lin, F. , Wu, Qiang, Liu, Ju, Wang, Dawei, and Kong, Xiangmao (2021). Path aggregation U-Net model for brain tumor segmentation. Multimedia Tools and Applications, 80(15):22951–22964.

journals Bib

@article{lin2021path,
  title = {Path aggregation U-Net model for brain tumor segmentation},
  author = {Lin, Fengming and Wu, Qiang and Liu, Ju and Wang, Dawei and Kong, Xiangmao},
  journal = {Multimedia Tools and Applications},
  volume = {80},
  number = {15},
  pages = {22951--22964},
  year = {2021},
  publisher = {Springer},
}

Lin, F. , Xia, Yan, Ravikumar, Nishant, Liu, Qiongyao, MacRaild, Michael, and 1 more author. (2023). Adaptive semi-supervised segmentation of brain vessels with ambiguous labels. International Conference on Medical Image Computing and Computer-Assisted Intervention:106–116.

conferences Bib

@article{lin2023adaptive,
  title = {Adaptive semi-supervised segmentation of brain vessels with ambiguous labels},
  author = {Lin, Fengming and Xia, Yan and Ravikumar, Nishant and Liu, Qiongyao and MacRaild, Michael and Frangi, Alejandro F},
  journal = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages = {106--116},
  year = {2023},
  publisher = {Springer},
}

Lin, F. , Xia, Yan, Deo, Yash, MacRaild, Michael, Dou, Haoran, and 4 more authors. (2024). Unsupervised Domain Adaptation for Brain Vessel Segmentation Through Transwarp Contrastive Learning. 2024 IEEE International Symposium on Biomedical Imaging (ISBI).

conferences Bib

@article{lin2024unsupervised,
  title = {Unsupervised Domain Adaptation for Brain Vessel Segmentation Through Transwarp Contrastive Learning},
  author = {Lin, Fengming and Xia, Yan and Deo, Yash and MacRaild, Michael and Dou, Haoran and Liu, Qiongyao and Wu, Kun and Ravikumar, Nishant and Frangi, Alejandro F},
  journal = {2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
  year = {2024},
  publisher = {IEEE},
}

Lin, F. , Xia, Yan, MacRaild, Michael, Deo, Yash, Dou, Haoran, and 4 more authors. (2024). GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation. 2024 IEEE International Symposium on Biomedical Imaging (ISBI).

conferences Bib

@article{lin2024gs,
  title = {GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation},
  author = {Lin, Fengming and Xia, Yan and MacRaild, Michael and Deo, Yash and Dou, Haoran and Liu, Qiongyao and Cheng, Nina and Ravikumar, Nishant and Frangi, Alejandro F},
  journal = {2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
  year = {2024},
  publisher = {IEEE},
}

Lin, F. , Liu, Ju, Wu, Qiang, Kong, Xiangmao, Khan, Waliullah, and 2 more authors. (2019). FMNet: feature mining networks for brain tumor segmentation. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI):555–560.

conferences Bib

@article{lin2019fmnet,
  title = {FMNet: feature mining networks for brain tumor segmentation},
  author = {Lin, Fengming and Liu, Ju and Wu, Qiang and Kong, Xiangmao and Khan, Waliullah and Shi, Wei and Pang, Enshuai},
  journal = {2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)},
  pages = {555--560},
  year = {2019},
  publisher = {IEEE},
}

Deo, Yash, Lin, F. , Dou, Haoran, Cheng, Nina, Ravikumar, Nishant, and 2 more authors. (2024). Few-shot learning in diffusion models for generating cerebral aneurysm geometries. 2024 IEEE International Symposium on Biomedical Imaging (ISBI).

conferences Bib

@article{deo2024few,
  title = {Few-shot learning in diffusion models for generating cerebral aneurysm geometries},
  author = {Deo, Yash and Lin, Fengming and Dou, Haoran and Cheng, Nina and Ravikumar, Nishant and Frangi, Alejandro F and Lassila, Toni},
  journal = {2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
  year = {2024},
  publisher = {IEEE},
}

Shi, Wei, Pang, Enshuai, Wu, Qiang, and Lin, F. . (2019). Brain tumor segmentation using dense channels 2D U-Net and multiple feature extraction network. International MICCAI brainlesion workshop:273–283.

conferences Bib

@article{shi2019brain,
  title = {Brain tumor segmentation using dense channels 2D U-Net and multiple feature extraction network},
  author = {Shi, Wei and Pang, Enshuai and Wu, Qiang and Lin, Fengming},
  journal = {International MICCAI brainlesion workshop},
  pages = {273--283},
  year = {2019},
  publisher = {Springer},
}

Kong, Xiangmao, Sun, Guoxia, Wu, Qiang, Liu, Ju, and Lin, F. . (2018). Hybrid pyramid u-net model for brain tumor segmentation. International conference on intelligent information processing:346–355.

conferences Bib

@article{kong2018hybrid,
  title = {Hybrid pyramid u-net model for brain tumor segmentation},
  author = {Kong, Xiangmao and Sun, Guoxia and Wu, Qiang and Liu, Ju and Lin, Fengming},
  journal = {International conference on intelligent information processing},
  pages = {346--355},
  year = {2018},
  publisher = {Springer},
}

Cheng, Nina, Liu, Zhengji, Deo, Yash, Dou, Haoran, Bi, Ning, and 5 more authors. (2024). Synthesising 3D cardiac CINE-MR images and corresponding segmentation masks using a latent diffusion model. 2024 IEEE International Symposium on Biomedical Imaging (ISBI).

conferences Bib

@article{cheng2024synthesising,
  title = {Synthesising 3D cardiac CINE-MR images and corresponding segmentation masks using a latent diffusion model},
  author = {Cheng, Nina and Liu, Zhengji and Deo, Yash and Dou, Haoran and Bi, Ning and Wu, Kun and Lin, Fengming and Taylor, Zeike A and Ravikumar, Nishant and Frangi, Alejandro F},
  journal = {2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
  year = {2024},
  publisher = {IEEE},
}

Chatterjee, Soumick, Mattern, Hendrik, Dorner, Marc, Sciarra, Alessandro, Dubost, Florian, and 8 more authors. (2024). SMILE UHURA Challenge Small Vessel Segmentation at Mesoscopic Scale from Ultra High Resolution 7T Magnetic Resonance Angiograms. arXiv preprint arXiv:2411.09593.

preprints Bib

@article{chatterjee2024smile,
  title = {SMILE UHURA Challenge Small Vessel Segmentation at Mesoscopic Scale from Ultra High Resolution 7T Magnetic Resonance Angiograms},
  author = {Chatterjee, Soumick and Mattern, Hendrik and Dorner, Marc and Sciarra, Alessandro and Dubost, Florian and Schnurre, Hannes and Khatun, Rupali and Yu, ChunChih and Hsieh, TsungLin and Tsai, YiShan and others and Lin, Fengming and others},
  journal = {arXiv preprint arXiv:2411.09593},
  year = {2024},
}

Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, and 8 more authors. (2018). Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629.

preprints Bib

@article{bakas2018identifying,
  title = {Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge},
  author = {Bakas, Spyridon and Reyes, Mauricio and Jakab, Andras and Bauer, Stefan and Rempfler, Markus and Crimi, Alessandro and Shinohara, Russell Takeshi and Berger, Christoph and Ha, Sung Min and Rozycki, Martin and others and Lin, Fengming and others},
  journal = {arXiv preprint arXiv:1811.02629},
  year = {2018},
}

Liu, Qiongyao, Lassila, Toni, Lin, F. , MacRaild, Michael, Patankar, Tufail, and 6 more authors. (2025). Key influencers in an aneurysmal thrombosis model: A sensitivity analysis and validation study. APL Bioengineering, 9(1).

journals Bib

@article{liu2025key,
  title = {Key influencers in an aneurysmal thrombosis model: A sensitivity analysis and validation study},
  author = {Liu, Qiongyao and Lassila, Toni and Lin, Fengming and MacRaild, Michael and Patankar, Tufail and Islim, Fathallah and Song, Shuang and Xu, Huanming and Chen, Xiang and Taylor, Zeike A and others},
  journal = {APL Bioengineering},
  volume = {9},
  number = {1},
  year = {2025},
  publisher = {AIP Publishing},
}

Liu, Qiongyao, Sarrami-Foroushani, Ali, Wang, Yongxing, MacRaild, Michael, Kelly, Christopher, and 6 more authors. (2023). Hemodynamics of thrombus formation in intracranial aneurysms: An in silico observational study. APL bioengineering, 7(3).

journals Bib

@article{liu2023hemodynamics,
  title = {Hemodynamics of thrombus formation in intracranial aneurysms: An in silico observational study},
  author = {Liu, Qiongyao and Sarrami-Foroushani, Ali and Wang, Yongxing and MacRaild, Michael and Kelly, Christopher and Lin, Fengming and Xia, Yan and Song, Shuang and Ravikumar, Nishant and Patankar, Tufail and others},
  journal = {APL bioengineering},
  volume = {7},
  number = {3},
  year = {2023},
  publisher = {AIP Publishing},
}