Search Results for author:
Found 38 papers, 2 papers with code
PULSAR Effect: Revealing Potential Synergies in Combined Radiation Therapy and Immunotherapy via Differential Equations
Therefore, a synergistic effect between immunotherapy and PULSAR is observed when the pulses are spaced out by a certain number of days.
Mathematical Modeling of the Synergetic Effect between Radiotherapy and Immunotherapy
Achieving effective synergy between radiotherapy and immunotherapy is critical for optimizing tumor control and treatment outcomes.
Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients
Accuracy, time-gain, and OOD detection are analyzed for our method in this particular application and compared with the popular MCDO and DE.
Thalamic nuclei segmentation from T$_1$-weighted MRI: unifying and benchmarking state-of-the-art methods with young and old cohorts
The thalamus and its constituent nuclei are critical for a broad range of cognitive and sensorimotor processes, and implicated in many neurological and neurodegenerative conditions.
Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer
Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.
Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation
With adaptation based on only the first three patients, the average DSCs were improved from 78. 6, 71. 9, 63. 0, 52. 2, 46. 3 and 69. 6 to 84. 4, 77. 8, 73. 0, 77. 8, 70. 5, 68. 1, for CTVstyle1, CTVstyle2, and CTVstyle3, Parotidsuperficial, Rectumsuperior, and Rectumposterior, respectively, showing the great potential of the Priorguided DDL network for a fast and effortless adaptation to new practice styles
Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
The bladder contour quality was primarily affected by using IV contrast.
Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Especially for smaller, single institutional datasets, it may be important to evaluate multiple estimations techniques before incorporating a model into clinical practice.
Deep Learning based Direct Segmentation Assisted by Deformable Image Registration for Cone-Beam CT based Auto-Segmentation for Adaptive Radiotherapy
We found that DL-based direct segmentation on CBCT trained with pseudo labels and without influencer volumes shows poor performance compared to DIR-based segmentation.
Region Specific Optimization (RSO)-based Deep Interactive Registration
A test time optimization (TTO) technique was proposed to further improve the DL models' performance.
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans.
Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
Firstly, we trained a population model with 200 patients, and then applied TTO to the remaining 39 test patients by refining the trained population model to obtain 39 individualized models.
A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Revision
The proposed clinical workflow of AIACR is as follows given an initial contour that requires a clinicians revision, the clinician indicates where a large revision is needed, and a trained deep learning (DL) model takes this input to update the contour.
Site-Agnostic 3D Dose Distribution Prediction with Deep Learning Neural Networks
Typically, the current dose prediction models are limited to small amounts of data and require re-training for a specific site, often leading to suboptimal performance.
Latent Space Arc Therapy Optimization
Volumetric modulated arc therapy planning is a challenging problem in high-dimensional, non-convex optimization.
Intentional Deep Overfit Learning (IDOL): A Novel Deep Learning Strategy for Adaptive Radiation Therapy
Finally, in the sCT reconstruction task, the MAE is reduced from 68 to 22 HU by utilizing the IDOL framework.
Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability
We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split.
PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume
A classifier is trained to identify which physician has contoured the CTV from just the contour and corresponding CT scan, to determine if physician styles are consistent and learnable.
Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning-based simulation study
In this study, we analyze the impact that variations in physician style have on dose to organs-at-risk(OAR) by simulating the clinical workflow via deep learning.
Deep Interactive Denoiser (DID) for X-Ray Computed Tomography
However, there exists two challenges regarding the DL-based denoisers: 1) a trained model typically does not generate different image candidates with different noise-resolution tradeoffs which sometimes are needed for different clinical tasks; 2) the model generalizability might be an issue when the noise level in the testing images is different from that in the training dataset.
Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm
As a result, the whole MC dose calculation pipeline can be finished within 0. 15 seconds, including both GPU MC dose calculation and deep learning based denoising, achieving the real time efficiency needed for some radiotherapy applications, such as online adaptive radiotherapy.
A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks
We propose to use Monte Carlo dropout (MCDO) and the bootstrap aggregation (bagging) technique on deep learning models to produce uncertainty estimations for radiation therapy dose prediction.
Dose Prediction with Deep Learning for Prostate Cancer Radiation Therapy: Model Adaptation to Different Treatment Planning Practices
For the transfer learning, we selected patient cases planned with three different styles from the same institution and one style from a different institution to adapt the source model to four target models.
Using Deep Learning to Predict Beam-Tunable Pareto Optimal Dose Distribution for Intensity Modulated Radiation Therapy
We studied and compared two different models, Model I and Model II.
Probabilistic self-learning framework for Low-dose CT Denoising
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 cause genetic and cancerous diseases.
A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy
Automating post-operative prostate CTV segmentation with traditional image segmentation methods has been a major challenge.
Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with Cone-Beam Computed Tomography (CBCT) to Computed Tomography (CT) image conversion
We trained a model on CBCT images acquired from one vendor's scanners for head and neck cancer patients and applied it to images from another vendor's scanners and for other disease sites.
A reinforcement learning application of guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy
We propose a reinforcement learning strategy using Monte Carlo Tree Search capable of finding a superior beam orientation set and in less time than CG. We utilized a reinforcement learning structure involving a supervised learning network to guide Monte Carlo tree search (GTS) to explore the decision space of beam orientation selection problem.
Mining Domain Knowledge: Improved Framework towards Automatically Standardizing Anatomical Structure Nomenclature in Radiotherapy
To solve these problems, we propose an automated structure nomenclature standardization framework, 3D Non-local Network with Voting (3DNNV).
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy
We propose a novel domain specific loss, which is a differentiable loss function based on the dose volume histogram, and combine it with an adversarial loss for the training of deep neural networks to generate Pareto optimal dose distributions.
Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations
However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database.
Intelligent Inverse Treatment Planning via Deep Reinforcement Learning, a Proof-of-Principle Study in High Dose-rate Brachytherapy for Cervical Cancer
While a treatment planning system can solve the optimization problem with given weights, adjusting the weights for high plan quality is performed by human.
Medical Physics
Generating Synthesized Computed Tomography (CT) from Cone-Beam Computed Tomography (CBCT) using CycleGAN for Adaptive Radiation Therapy
Dose calculation accuracy using sCT images has been improved over the original CBCT images, with the average Gamma Index passing rate increased from 95. 4% to 97. 4% for 1 mm/1% criteria.
Medical Physics
Fully Automated Organ Segmentation in Male Pelvic CT Images
Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning.
Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture
The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, multiple prescription dose levels, and many radiation-sensitive critical structures near the target.
Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning
Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace.
A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time.
