762 citations · 875 across the 37 of their papers we have counts for
39 papers
LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification
Yuwen Chen, Yaqian Chen, Roy Colglazier +4
Lower extremity computed tomography (CT) contains clinically relevant information for body composition analysis, sarcopenia assessment, and musculoskeletal disease monitoring, but…
Fully Automated Deep Learning Based Glenoid Bone Loss Measurement and Severity Stratification on 3D CT in Shoulder Instability
Zhonghao Liu, Hanxue Gu, Qihang Li +4
To develop and validate a fully automated, deep-learning pipeline for measuring glenoid bone loss on 3D CT scans using linear-based, en-face view, and best-circle method. Shoulder…
Transplant-Ready? Evaluating AI Lung Segmentation Models in Candidates with Severe Lung Disease
Jisoo Lee, Michael R. Harowicz, Yuwen Chen +5
This study evaluates publicly available deep-learning based lung segmentation models in transplant-eligible patients to determine their performance across disease severity levels,…
Rethinking Pulmonary Embolism Segmentation: A Study of Current Approaches and Challenges with an Open Weight Model
Yixin Zhang, Ryan Chamberlain, Lawrence Ngo +2
Pulmonary Embolism (PE) is a life-threatening condition for which accurate and timely detection is critical to patient care. However, our systematic study of PE segmentation algori…
Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Hanxue Gu, Yaqian Chen, Nicholas Konz +2
Foundation models, pre-trained on large image datasets and capable of capturing rich feature representations, have recently shown potential for zero-shot image registration. Howeve…
MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Haoyu Dong, Yuwen Chen, Hanxue Gu +4
The widespread use of Magnetic Resonance Imaging (MRI) in combination with deep learning shows promise for many high-impact automated diagnostic and prognostic tools. However, trai…
BreastSegNet: Multi-label Segmentation of Breast MRI
Qihang Li, Jichen Yang, Yaqian Chen +4
Breast MRI provides high-resolution imaging critical for breast cancer screening and preoperative staging. However, existing segmentation methods for breast MRI remain limited in s…
Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery
Hanxue Gu, Yaqian Chen, Jisoo Lee +5
Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after colectomy, either alone or combi…
SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI
Roy Colglazier, Jisoo Lee, Haoyu Dong +12
The quantity and quality of muscles are increasingly recognized as important predictors of health outcomes. While MRI offers a valuable modality for such assessments, obtaining pre…
GuidedMorph: Two-Stage Deformable Registration for Breast MRI
Yaqian Chen, Hanxue Gu, Haoyu Dong +5
Accurately registering breast MR images from different time points enables the alignment of anatomical structures and tracking of tumor progression, supporting more effective breas…
Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2
Yuwen Chen, Zafer Yildiz, Qihang Li +5
Manual annotation of volumetric medical images, such as magnetic resonance imaging (MRI) and computed tomography (CT), is a labor-intensive and time-consuming process. Recent advan…
Breast density in MRI: an AI-based quantification and relationship to assessment in mammography
Yaqian Chen, Lin Li, Hanxue Gu +5
Mammographic breast density is a well-established risk factor for breast cancer. Recently there has been interest in breast MRI as an adjunct to mammography, as this modality provi…
Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis
Yaqian Chen, Hanxue Gu, Yuwen Chen +7
Body composition assessment using CT images can potentially be used for a number of clinical applications, including the prognostication of cardiovascular outcomes, evaluation of m…
How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?
Yixin Zhang, Kevin Kramer, Maciej A. Mazurowski
Automated segmentation of medical images heavily relies on the availability of precise manual annotations. However, generating these annotations is often time-consuming, expensive,…
Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects
Yixin Zhang, Nicholas Konz, Kevin Kramer +1
Image segmentation foundation models (SFMs) like Segment Anything Model (SAM) have achieved impressive zero-shot and interactive segmentation across diverse domains. However, they…
Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets
Nicholas Konz, Richard Osuala, Preeti Verma +16
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, t…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Study
Brian Guo, Darui Lu, Gregory Szumel +4
Purpose: Medical images acquired using different scanners and protocols can differ substantially in their appearance. This phenomenon, scanner domain shift, can result in a drop in…
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports
Mohamed Sobhi Jabal, Pranav Warman, Jikai Zhang +6
Purpose: To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weights large langu…
SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images
Zafer Yildiz, Yuwen Chen, Maciej A. Mazurowski
Creating annotations for 3D medical data is time-consuming and often requires highly specialized expertise. Various tools have been implemented to aid this process. Segment Anythin…
Segment anything model 2: an application to 2D and 3D medical images
Haoyu Dong, Hanxue Gu, Yaqian Chen +3
Segment Anything Model (SAM) has gained significant attention because of its ability to segment various objects in images given a prompt. The recently developed SAM 2 has extended…
Pre-processing and Compression: Understanding Hidden Representation Refinement Across Imaging Domains via Intrinsic Dimension
Nicholas Konz, Maciej A. Mazurowski
In recent years, there has been interest in how geometric properties such as intrinsic dimension (ID) of a neural network's hidden representations change through its layers, and ho…
Rethinking Perceptual Metrics for Medical Image Translation
Nicholas Konz, Yuwen Chen, Hanxue Gu +2
Modern medical image translation methods use generative models for tasks such as the conversion of CT images to MRI. Evaluating these methods typically relies on some chosen downst…
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 +1
Automated segmentation is a fundamental medical image analysis task, which enjoys significant advances due to the advent of deep learning. While foundation models have been useful…