35 citations · 120 across the 15 of their papers we have counts for
15 papers
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution
Shaohua Zhi, Yinghui Wang, Haonan Xiao +7
Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for tumor motion management in image-guided radiation therapy (IGRT). However, current 4D-MRI suffers…
Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation
Anjali Balagopal, Dan Nguyen, Ti Bai +3
When a pre-trained general auto-segmentation model is deployed at a new institution, a support framework in the proposed Prior-guided DDL network will learn the systematic differen…
Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Michael Dohopolski, Kai Wang, Biling Wang +5
Prediction uncertainty estimation has clinical significance as it can potentially quantify prediction reliability. Clinicians may trust 'blackbox' models more if robust reliability…
Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
Biling Wang, Michael Dohopolski, Ti Bai +10
We evaluated the temporal performance of a deep learning (DL) based artificial intelligence (AI) model for auto segmentation in prostate radiotherapy, seeking to correlate its effi…
Deep Learning based Direct Segmentation Assisted by Deformable Image Registration for Cone-Beam CT based Auto-Segmentation for Adaptive Radiotherapy
Xiao Liang, Howard Morgan, Ti Bai +3
Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate auto-segmentation to reduce the time cost for physicians to edit contours. However, deep learning (DL)-bas…
Region Specific Optimization (RSO)-based Deep Interactive Registration
Ti Bai, Muhan Lin, Xiao Liang +5
Medical image registration is a fundamental and vital task which will affect the efficacy of many downstream clinical tasks. Deep learning (DL)-based deformable image registration…
Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
Xiao Liang, Jaehee Chun, Howard Morgan +4
Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) imag…
S2MS: Self-Supervised Learning Driven Multi-Spectral CT Image Enhancement
Chaoyang Zhang, Shaojie Chang, Ti Bai +1
Photon counting spectral CT (PCCT) can produce reconstructed attenuation maps in different energy channels, reflecting energy properties of the scanned object. Due to the limited p…
A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Revision
Ti Bai, Anjali Balagopal, Michael Dohopolski +7
Automatic segmentation of anatomical structures is critical for many medical applications. However, the results are not always clinically acceptable and require tedious manual revi…
Deep High-Resolution Network for Low Dose X-ray CT Denoising
Ti Bai, Dan Nguyen, Biling Wang +1
Low Dose Computed Tomography (LDCT) is clinically desirable due to the reduced radiation to patients. However, the quality of LDCT images is often sub-optimal because of the inevit…
A Feasibility Study on Deep Learning Based Individualized 3D Dose Distribution Prediction
Jianhui Ma, Dan Nguyen, Ti Bai +5
Purpose: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An optimal dose distribution based on a specific anatomy can be predicted by pre-t…
Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm
Ti Bai, Biling Wang, Dan Nguyen +1
Monte Carlo (MC) simulation is considered the gold standard method for radiotherapy dose calculation. However, achieving high precision requires a large number of simulation histor…
Deep Interactive Denoiser (DID) for X-Ray Computed Tomography
Ti Bai, Biling Wang, Dan Nguyen +5
Low dose computed tomography (LDCT) is desirable for both diagnostic imaging and image guided interventions. Denoisers are openly used to improve the quality of LDCT. Deep learning…
Probabilistic self-learning framework for Low-dose CT Denoising
Ti Bai, Dan Nguyen, Biling Wang +1
Despite the indispensable role of X-ray computed tomography (CT) in diagnostic medicine field, the associated ionizing radiation is still a major concern considering that it may ca…
Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance
Yuan Gao, Xingyuan Bu, Yang Hu +4
This report demonstrates our solution for the Open Images 2018 Challenge. Based on our detailed analysis on the Open Images Datasets (OID), it is found that there are four typical…