1 code implementation • 15 Apr 2024 • Hanxue Gu, Haoyu Dong, Jichen Yang, Maciej A. Mazurowski
Automated segmentation is a fundamental medical image analysis task, which enjoys significant advances due to the advent of deep learning.
no code implementations • 10 Apr 2024 • Nicholas Konz, YuWen Chen, Hanxue Gu, Haoyu Dong, Maciej A. Mazurowski
Modern medical image translation methods use generative models for tasks such as the conversion of CT images to MRI.
no code implementations • 18 Mar 2024 • Keyu Li, Hanxue Gu, Roy Colglazier, Robert Lark, Elizabeth Hubbard, Robert French, Denise Smith, Jikai Zhang, Erin McCrum, Anthony Catanzano, Joseph Cao, Leah Waldman, Maciej A. Mazurowski, Benjamin Alman
To address these challenges and the lack of interpretability found in certain existing automated methods, we have created fully automated software that not only precisely measures the Cobb angle but also provides clear visualizations of these measurements.
no code implementations • 16 Mar 2024 • YuWen Chen, Nicholas Konz, Hanxue Gu, Haoyu Dong, Yaqian Chen, Lin Li, Jisoo Lee, Maciej A. Mazurowski
We evaluate our method by training a segmentation model on images translated from CT to MRI with their original CT masks and testing its performance on real MRIs.
1 code implementation • 14 Feb 2024 • Haoyu Dong, Nicholas Konz, Hanxue Gu, Maciej A. Mazurowski
Here, we approach such a task, of adapting a medical image segmentation model with only a single unlabeled test image.
1 code implementation • 23 Jan 2024 • Hanxue Gu, Roy Colglazier, Haoyu Dong, Jikai Zhang, Yaqian Chen, Zafer Yildiz, YuWen Chen, Lin Li, Jichen Yang, Jay Willhite, Alex M. Meyer, Brian Guo, Yashvi Atul Shah, Emily Luo, Shipra Rajput, Sally Kuehn, Clark Bulleit, Kevin A. Wu, Jisoo Lee, Brandon Ramirez, Darui Lu, Jay M. Levin, Maciej A. Mazurowski
In our study, we propose a versatile, publicly available deep-learning model for bone segmentation in MRI across multiple standard MRI locations.
no code implementations • 17 Dec 2023 • Yixin Zhang, Shen Zhao, Hanxue Gu, Maciej A. Mazurowski
In situations where unlimited annotation time was available, precise annotations still lead to the highest segmentation model performance.
no code implementations • 28 Jun 2023 • Hanxue Gu, Haoyu Dong, Nicholas Konz, Maciej A. Mazurowski
We experimentally study the effects of different aspects of F-B imbalance (object size, number of objects, dataset size, object type) on detection performance.
2 code implementations • 20 Apr 2023 • Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, Yixin Zhang
We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others.
no code implementations • 13 Mar 2023 • Hanxue Gu, Hongyu He, Roy Colglazier, Jordan Axelrod, Robert French, Maciej A Mazurowski
Three-dimensional segmentation in magnetic resonance images (MRI), which reflects the true shape of the objects, is challenging since high-resolution isotropic MRIs are rare and typical MRIs are anisotropic, with the out-of-plane dimension having a much lower resolution.
1 code implementation • 6 Jul 2022 • Nicholas Konz, Hanxue Gu, Haoyu Dong, Maciej A. Mazurowski
These results give a more principled underpinning for the intuition that radiological images can be more challenging to apply deep learning to than natural image datasets common to machine learning research.
1 code implementation • 16 Mar 2022 • Hanxue Gu, Keyu Li, Roy J. Colglazier, Jichen Yang, Michael Lebhar, Jonathan O'Donnell, William A. Jiranek, Richard C. Mather, Rob J. French, Nicholas Said, Jikai Zhang, Christine Park, Maciej A. Mazurowski
We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score.
no code implementations • 22 Nov 2021 • Yifan Zhang, Haoyu Dong, Nicholas Konz, Hanxue Gu, Maciej A. Mazurowski
Specifically, we propose a novel modification of visual transformer (ViT) on image feature patches to connect the feature patches of a tumor with healthy backgrounds of breast images and form a more robust backbone for tumor detection.