Publications (23)
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas, Mauricio Reyes, Andras Jakab +421
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritum…
Novel Method for Background Phase Removal on MRI Proton Resonance Frequency Measurements
Wolfgang Stefan, David Fuentes, Erol Yeniaras +3
MR images have a magnitude and a phase, but in almost all clinical applications only the magnitude images are used, because the phase images have a smooth but strong background sig…
Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks
Adrian Celaya, Yimo Wang, David Fuentes +1
In recent years, there has been an increasing interest in using deep learning and neural networks to tackle scientific problems, particularly in solving partial differential equati…
Precision in the Face of Noise -- Lessons from Kahneman, Siboney, and Sunstein for Radiation Oncology
Kareem A. Wahid, Clifton D. Fuller, David Fuentes
In this manuscript, we draw on the insights from Kahneman, Sibony, and Sunsteins influential nonfiction book Noise: A Flaw in Human Judgment to explore the concept of unwanted vari…
PocketNet: A Smaller Neural Network for Medical Image Analysis
Adrian Celaya, Jonas A. Actor, Rajarajeswari Muthusivarajan +5
Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNe…
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…
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy
Rajarajeswari Muthusivarajan, Adrian Celaya, Joshua P. Yung +4
Deep neural networks with multilevel connections process input data in complex ways to learn the information.A networks learning efficiency depends not only on the complex neural n…
Resection cavity auto-contouring for patients with pediatric medulloblastoma using only CT information
Soleil Hernandez, Callistus Nguyen, Skylar Gay +9
Purpose: Target delineation for radiation therapy is a time-consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, th…
Accelerated Magnetic Resonance Thermometry in Presence of Uncertainties
Reza Madankan, Wolfgang Stefan, Samuel Fahrenholtz +6
An accelerated model-based information theoretic approach is presented to perform the task of Magnetic Resonance (MR) thermal image reconstruction from a limited number of observed…
Adaptive Collocation Point Strategies For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method
Adrian Celaya, David Fuentes, Beatrice Riviere
Physics-informed neural networks (PINNs) have gained significant attention for solving forward and inverse problems related to partial differential equations (PDEs). While advancem…
A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation
Adrian Celaya, Beatrice Riviere, David Fuentes
Within medical imaging segmentation, the Dice coefficient and Hausdorff-based metrics are standard measures of success for deep learning models. However, modern loss functions for…
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…
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation
Adrian Celaya, Beatrice Riviere, David Fuentes
Accurate medical imaging segmentation is critical for precise and effective medical interventions. However, despite the success of convolutional neural networks (CNNs) in medical i…
Distributed Conditional GAN (discGAN) For Synthetic Healthcare Data Generation
David Fuentes, Diana McSpadden, Sodiq Adewole
In this paper, we propose a distributed Generative Adversarial Networks (discGANs) to generate synthetic tabular data specific to the healthcare domain. While using GANs to generat…
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…
Risk Prediction in Cancer Imaging Using Enriched Radiomics Features
Alec Reinhardt, Tsung-Hung Yao, Raven Hollis +10
Background: We aim to develop enriched radiomics features that integrate classical structural radiomics with novel functional radiomics derived from liver MRI for diagnosis and ris…
A Priori Generalizability Estimate for a CNN
Cito Balsells, Beatrice Riviere, David Fuentes
We formulate truncated singular value decompositions of entire convolutional neural networks. We demonstrate the computed left and right singular vectors are useful in identifying…
Two Stage Segmentation of Cervical Tumors using PocketNet
Awj Twam, Adrian E. Celaya, Megan C. Jacobsen +6
Cervical cancer remains the fourth most common malignancy amongst women worldwide.1 Concurrent chemoradiotherapy (CRT) serves as the mainstay definitive treatment regimen for local…
1D Thermoembolization Model Using CT Imaging Data for Porcine Liver
Rohan Amare, Danielle Stolley, Steve Parrish +7
Objective: Innovative therapies such as thermoembolization are expected to play an important role in improvising care for patients with diseases such as hepatocellular carcinoma. T…
Solutions to Elliptic and Parabolic Problems via Finite Difference Based Unsupervised Small Linear Convolutional Neural Networks
Adrian Celaya, Keegan Kirk, David Fuentes +1
In recent years, there has been a growing interest in leveraging deep learning and neural networks to address scientific problems, particularly in solving partial differential equa…
Deep Learning-Based Automated Post-Operative Gross Tumor Volume Segmentation in Glioblastoma Patients
Rajarajeswari Muthusivarajan, Adrian Celaya, Maguy Farhat +8
Precise automated delineation of post-operative gross tumor volume in glioblastoma cases is challenging and time-consuming owing to the presence of edema and the deformed brain tis…
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…
Automation of Radiation Treatment Planning for Rectal Cancer
Kai Huang, Prajnan Das, Adenike M. Olanrewaju +9
To develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy treatment planning that combines deep-learning(DL) aperture predictions and forward-plan…