386 citations · 637 across the 17 of their papers we have counts for
18 papers
Pre- and Post-Treatment Glioma Segmentation with the Medical Imaging Segmentation Toolkit
Adrian Celaya, Tucker Netherton, Dawid Schellingerhout +3
Medical image segmentation continues to advance rapidly, yet rigorous comparison between methods remains challenging due to a lack of standardized and customizable tooling. In this…
Virtual Dosimetrists: A Radiotherapy Training "Flight Simulator"
Skylar S. Gay, Tucker Netherton, Barbara Marquez +7
Effective education in radiotherapy plan quality review requires a robust, regularly updated set of examples and the flexibility to demonstrate multiple possible planning approache…
When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT
Matt Y. Cheung, Sophia Zorek, Tucker J. Netherton +4
Diffusion models demonstrate state-of-the-art performance on image generation, and are gaining traction for sparse medical image reconstruction tasks. However, compared to classica…
Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation
McKell Woodland, Nihil Patel, Austin Castelo +13
Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these mode…
Bias-Aware Conformal Prediction for Metric-Based Imaging Pipelines
Matt Y. Cheung, Tucker J. Netherton, Laurence E. Court +2
Reliable confidence measures of metrics derived from medical imaging reconstruction pipelines would improve the standard of decision-making in many clinical workflows. Conformal Pr…
DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rays
Yiran Sun, Hana Baroudi, Tucker Netherton +4
Computed Tomography (CT) scans are the standard-of-care for the visualization and diagnosis of many clinical ailments, and are needed for the treatment planning of external beam ra…
MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework
Adrian Celaya, Evan Lim, Rachel Glenn +7
Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of st…
Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction
Matt Y Cheung, Tucker J Netherton, Laurence E Court +2
Modern deep learning reconstruction algorithms generate impressively realistic scans from sparse inputs, but can often produce significant inaccuracies. This makes it difficult to…
Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification
Kareem A. Wahid, Carlos E. Cardenas, Barbara Marquez +8
Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three…
CT Reconstruction from Few Planar X-rays with Application towards Low-resource Radiotherapy
Yiran Sun, Tucker Netherton, Laurence Court +2
CT scans are the standard-of-care for many clinical ailments, and are needed for treatments like external beam radiotherapy. Unfortunately, CT scanners are rare in low and mid-reso…
Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation
McKell Woodland, Nihil Patel, Mais Al Taie +4
Clinically deployed segmentation models are known to fail on data outside of their training distribution. As these models perform well on most cases, it is imperative to detect out…
Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans
Mary P. Gronberg, Beth M. Beadle, Adam S. Garden +20
Purpose: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. Methods: A total of 245 VMAT HN plans…
Automated WBRT Treatment Planning via Deep Learning Auto-Contouring and Customizable Landmark-Based Field Aperture Design
Yao Xiao, Carlos Cardenas, Dong Joo Rhee +12
In this work, we developed and evaluated a novel pipeline consisting of two landmark-based field aperture generation approaches for WBRT treatment planning; they are fully automate…
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
Aaron Babier, Rafid Mahmood, Binghao Zhang +56
We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients w…
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…
Distinguishing and electron pairing symmetries by neutron spin resonance in superconducting NaFeCoAs
Chenglin Zhang, H. -F. Li, Yu Song +17
A determination of the superconducting (SC) electron pairing symmetry forms the basis for establishing a microscopic mechansim for superconductivity. For iron pnictide superconduct…
Strong-Coupling Superconductivity in NaFeCoAs: the Eliashberg Theory and Beyond
Guotai Tan, Ping Zheng, Xiancheng Wang +8
We study the normal-state and superconducting properties of NaFeCoAs system by specific heat measurements. Both the normal-state Sommerfeld coefficient and superconduct…
Spin Waves and magnetic exchange interactions in insulating RbFeSe
Miaoyin Wang, Chen Fang, Dao-Xin Yao +10
The discovery of alkaline iron selenide FeSe ( K, Rb, Cs) superconductors has generated considerable excitement in the condensed matter physics community becau…