Congrats to Nick Konz for defending his PhD dissertation! Nick has done an amazing job developing a variety of machine learning algorithms in the context of breast imaging. Nick's next step will be a postdoc at UNC Chapel Hill, where he will continue working on machine learning.

NEW PUBLIC DATASET ALERT! Just published in Nature Scientific Data. We're happy to publicly release another medical imaging dataset: Duke University Cervical Spine MRI Segmentation Dataset (CSpineSeg). Here are some details:

Congrats to Hanxue Gu, Yaqian Chen, and co-authors for receiving the best paper award at the MICCAI Deep Breath 2025 workshop! The paper discusses the use of foundation models in the context of image registration.

Precise 3D measurement of fracture angles would be of enormous help in orthopedics, and yet it's very challenging from standard X-rays. We have a solution!

Want to make a CT out of an MRI? It's possible thanks to generative models, but it has issues which we're addressing in our ContourDiff model (code available)!

Segmentation models may perform poorly when test images belong to a different domain (e.g., a different medical center). We developed a method of adapting the models using a single unlabeled image from the test domain!

Tired of annotating entire volumes of medical images? We developed a model allowing for annotating one slice and extending the annotation to the entire volume.

Are vision foundation models worthwhile using? If so, how to fine-tune them? We studied this extensively in our recent paper now published in MELBA. We evaluated various fine-tuning algorithms using 17 medical imaging datasets, with both task-specific fine-tuning and self-supervised learning.

1/9 Introducing Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets, led by @nickkonz.bsky.social and Richard Osuala. Our paper can be found at arxiv.org/abs/2412.01496, and you can easily compute FRD yourself with our code at github.com/RichardObi/f...

Announcing another new public segmentation model from our team! It segments muscles and fat (different types) in CTs of the chest/abdomen/pelvis.

Welcome! I'm new here. I'm a faculty member at Duke, leading a lab that focuses on AI in medical imaging. I'm also the director of Duke Spark Initiative for AI in Medical Imaging. Follow me if you would like to hear about the recent algorithms that we share.