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Results for "dice score": 49 found

A New Voxel-Based Similarity Approach for Assessing Contour Similarity and Clinical Dosimetric Effect

Authors: Shari Damast, Svetlana Kuznetsova, Christopher J. Tien

Affiliation: Yale University School of Medicine, Department of Therapeutic Radiology, Yale University School of Medicine

Abstract Preview: Purpose: Current contour similarity evaluation approaches (Dice Similarity Coefficient, Mean Distance to Agreement) are limited to geometric agreement without assessment of ultimate dosimetric impact....

A SAM-Guided and Match-Based Semi-Supervised Segmentation Framework for Medical Imaging

Authors: Weiguo Lu, Jax Luo, Xiaoxue Qian, Hua-Chieh Shao, Guoping Xu, You Zhang

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Harvard Medical School

Abstract Preview: Purpose:
Semi-supervised segmentation leverages sparse annotation information to learn rich representations from combined labeled and label-less data for segmentation tasks. This study leverages th...

Advanced Daily Imaging Technology Improves Tracking of Soft Tissue Decomposition for Head and Neck Cancer Patients Receiving Radiotherapy

Authors: Robert K Chin, Klea Hoxha, Erika Jank, Jesus Juarez, Eulanca Yuka Liu, Dylan P. O'Connell, X. Sharon Qi, Ricky R Savjani

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, University of California, Los Angeles, UCLA Radiation Oncology

Abstract Preview: Purpose:
Daily cone beam CT (CBCT) images are acquired prior to each radiotherapy treatment for verifying patient positioning. HyperSight is an advanced imaging solution that replaces standard CBCT...

An Automated Tool for the Categorization of a Clinical Database By Anatomic Region for Big Data Applications

Authors: Yasin Abdulkadir, Justin Hink, James M. Lamb, Jack Neylon

Affiliation: Department of Radiation Oncology, University of California, Los Angeles

Abstract Preview: Purpose: Curation remains a significant barrier to the use of ‘big data’ radiotherapy planning databases of 100,000 patients or more. Anatomic site of treatment is an important stratification for almo...

Automated Framework for Predicting Tumour Growth in Vestibular Schwannomas Using Contrast-Enhanced T1-Weighted MRI

Authors: Mehdi Amini, Minerva Becker, Simina Chiriac, Alexandre Cusin, Dimitrios Daskalou, Ghasem Hajianfar, Sophie Neveu, Marcella Pucci, Yazdan Salimi, Pascal Senn, Habib Zaidi

Affiliation: Geneva University Hospital, Division of Radiology, Diagnostic Department, Geneva University Hospitals, Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences, Geneva University Hospitals

Abstract Preview: Purpose: Personalized prediction of vestibular schwannoma (VS) tumour growth is crucial for guiding patient management decisions toward observation versus intervention. This study proposes an automate...

Automated MR Segmentation for Online Adaptive MR-Linac Therapy Using an in-House Model

Authors: David L. Barbee, David Byun, Matt Long, Jose R. Teruel Antolin, Michael J Zelefsky

Affiliation: NYU Langone Health

Abstract Preview: Purpose:
Online adaptive MR-Linac therapy requires contour adaptation, often adding 20 minutes to treatment time and reducing machine throughput. This study introduces a fully automated MR contour ...

BEST IN PHYSICS IMAGING: Population-Based Cardio-Respiratory Motion Model to Simulate 4D CT Angiography and 2D+t Fluoroscopy for Percutaneous Coronary Intervention

Authors: Debarghya China, Junghoon Lee, Ali Uneri

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Department of Biomedical Engineering, Johns Hopkins University, Johns Hopkins Univ

Abstract Preview: Purpose: This study aims to develop a population-based cardio-respiratory motion model and apply it to patient-specific 3D CTA to simulate 4D CTA and 2D+t fluoroscopy sequences. The developed motion m...

BEST IN PHYSICS MULTI-DISCIPLINARY: Building a Cross-Modality Model to Integrate Bio-Clinical Features, Anatomical MRI, and White-Matter Pathlength Mapping for Personalized Glioblastoma RT Planning

Authors: Steve Braunstein, Angela Jakary, Hui Lin, Bo Liu, Janine Lupo, Tiffany Ngan, Ke Sheng, Nate Tran

Affiliation: Radiation Oncology, University of California San Francisco, Graduate Program in Bioengineering, University of California San Francisco-UC Berkeley, Department of Radiation Oncology, University of California San Francisco, Department of Radiology and Biomedical Imaging, University of California San Francisco, Department of Radiation Oncology, University of California, San Francisco

Abstract Preview: Purpose: Current RT clinical target volumes (CTVs) for Glioblastoma (GBM) employ 2cm isotropic expansions of gross tumor volumes. However, studies showed patients still experience progression beyond t...

BEST IN PHYSICS MULTI-DISCIPLINARY: Foundation Model-Empowered Unsupervised 3D Deformable Medical Image Registration

Authors: Xianjin Dai, PhD, Zhuoran Jiang, Lei Ren, Lei Xing, Zhendong Zhang

Affiliation: University of Maryland School of Medicine, Department of Radiation Oncology, Stanford University, Duke University, Stanford University

Abstract Preview: Purpose: Unsupervised deep learning has shown great promise in deformable image registration (DIR). These methods update model weights to optimize image similarity without necessitating ground truth d...

Box-Prompt Zero-Shot Smart Segmentation in Radiation Oncology Using a SAM-Based Model: Smartsam

Authors: Kristen A. Duke, Samer Jabor, Neil A. Kirby, Parker New, Niko Papanikolaou, Arkajyoti Roy, Yuqing Xia

Affiliation: St. Mary's University, The University of Texas San Antonio, UT Health San Antonio

Abstract Preview: Purpose:
The Segment Anything Model (SAM) is a foundational box-prompt-based model for natural image segmentation. However, its applicability to zero-shot 3D medical image segmentation, particularl...

Brain Vessel Segmentation and Tracking in Longitudinal Glioblastoma MRI Scans

Authors: Evan Calabrese, Edward Robert Criscuolo, Deshan Yang

Affiliation: Duke University, Department of Radiation Oncology, Duke University

Abstract Preview: Purpose: Glioblastoma (GBM) is the most common and aggressive form of brain cancer. Deformable image registration (DIR) is a powerful tool to compute anatomical changes in longitudinal MRI scans, whic...

Clinical Validation of a Deep-Learning Segmentation Tool for Head and Neck Cancer Patients and Thoracic and Abdominal Cancer Patients

Authors: Haijian Chen, Katja M. Langen, William Andrew LePain, Claire Tran, Mingyao Zhu

Affiliation: Emory Healthcare, Emory University, Georgia Institute of Technology

Abstract Preview: Purpose: To validate the performance of a commercial deep-learning segmentation (DLS) tool for head and neck cancer (HNC) and thoracic and abdominal cancer (TAC) by comparing it to manual segmentation...

Comparison of AI-Based and Ants for Longitudinal Deformable Image Registration in Head and Neck Cancer

Authors: Aditya P. Apte, Joseph O. Deasy, Jue Jiang, Nancy Lee, Sudharsan Madhavan, Nishant Nadkarni, Lopamudra Nayak, Harini Veeraraghavan, Wei Zhao

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: To track early response to radiotherapy using digital twins, it is crucial to quantify tumor volume and mass changes. Traditional tumor detection methods, particularly in image registration, ...

Contrast-Free Enhancement of Coronary Artery Stenosis: Synthetic Ccta from Non-Contrast CT Using Diffusion Model

Authors: Abdusalam Abdukerim

Affiliation: Institute for Medical Imaging Technology, Ruijin Hospital

Abstract Preview: Purpose:
Coronary computed tomography angiography (CCTA) is the gold-standard non-invasive test for coronary artery disease (CAD), but iodine contrast agents (ICA) pose limitations in specific popu...

Contrast-Free Full Intracranial Vessel Geometry Estimation from MRI with Metric Learning-Based Inference

Authors: Zhaoyang Fan, Eric Nguyen, Dan Ruan, Jiayu Xiao

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, Department of Radiology, University of Southern California, University of Southern California

Abstract Preview: Purpose: MR vessel wall imaging (VWI) has been shown to be effective for evaluating intracranial atherosclerosis disease. However, VWI typically also requires an MR angiography (MRA) in the same imagi...

Cycle-Consistent Multi-Task Automated Segmentation and Synthetic CT Generation Model for Adaptive Proton Therapy

Authors: Derek Tang, Susu Yan

Affiliation: Massachusetts General Hospital

Abstract Preview: Purpose: To evaluate the performance of a multi-task automated-segmentation and synthetic CT generation model (sCT) and investigate its application in an adaptive proton therapy workflow.
Methods: ...

Deep Learning-Based Auto Segmentation of Oars in Head and Neck Radiation Therapy

Authors: Laila A Gharzai, Bharat B Mittal, Poonam Yadav

Affiliation: Northwestern Feinberg School of Medicine, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Northwestern University Feinberg School of Medicine

Abstract Preview: Purpose: Multiple studies have shown the increasing role of deep learning in segmenting regions of interest. This work presents the feasibility of auto-segmenting the critical structures for head and ...

Deep-Dive Comparative Assessment between Digitally Reconstructed Radiographs and X-Ray Digital Radiographs from Lung CT Scans

Authors: Xinyi Fu, Dan Ruan, Ke Sheng

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, University of California, San Francisco, Department of Radiation Oncology, University of California, San Francisco

Abstract Preview: Purpose:
Digitally reconstructed radiographs (DRRs) are easy to generate and widely used to establish research protocols in pulmonary diagnosis and image-guided radiotherapy tasks. A question remai...

Deep-Learning Convolutional Neural Network-Based Breast Cancer Localization for Mammographic Images: A Study on Simulated and Clinical Images

Authors: Xiaoyu Duan, Xiang Li, Wenbo Wan, Lei Zhang, Yiwen Zhang

Affiliation: Duke University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Breast screening has been proved to reduce breast cancer mortality by early detection and treatment for patients. Mammography is the most common and widely used technique for breast cancer sc...

Dosimetric Impact of Auto-Segmentation with Replanning: An Analysis of a Prospective Clinical Trial

Authors: Kathryn J. Dess, Martha M. Matuszak, Dan Polan

Affiliation: University of Michigan

Abstract Preview: Purpose: A recent survey demonstrated 18 of 20 top academic institutions have implemented auto-segmentation. Studies to date have focused on geometric contour changes and dosimetric differences using ...

Enhancing Proton Treatment and Mitigating Radiation-Induced Lung Injury Using a Novel Cycle Diffusion Approach for Lung Ventilation Estimation

Authors: Yang Lei, Haibo Lin, Tian Liu, Charles B. Simone, Shouyi Wei, Ajay Zheng

Affiliation: Icahn School of Medicine at Mount Sinai, New York Proton Center

Abstract Preview: Purpose: Radiation-induced lung injury (RILI), encompassing pneumonitis and fibrosis, represents a critical dose-limiting factor in lung cancer radiation therapy. Variability in treatment outcomes is ...

Evaluating Deep Learning Models for Accurate Segmentation of GTV and Oars in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer

Authors: Christopher G. Ainsley, Pradeep Bhetwal, Yingxuan Chen, Wookjin Choi, Vimal K. Desai, Karen E. Mooney, Adam Mueller, Hamidreza Nourzadeh, Yevgeniy Vinogradskiy, Maria Werner-Wasik

Affiliation: Thomas Jefferson University

Abstract Preview: Purpose: MR-guided adaptive radiotherapy (MRgART) has demonstrated improved outcomes for patients with pancreatic cancer. However, the time-consuming re-segmentation of targets and organs-at-risk (OAR...

Evaluating the Impact of Different Deface Algorithms on the Deep Learning Segmentation Software Performance

Authors: Ali Ammar, Quan Chen, Yi Rong, Libing Zhu

Affiliation: Mayo Clinic Arizona

Abstract Preview: Purpose: To investigate how defacing algorithms, essential for patient privacy in data sharing, impact AI-based segmentation performance in CT imaging for radiation therapy. This study evaluates wheth...

High Resolution Head Motion Correction Based on Pilot Tone Signals – a Calibration-Free Method

Authors: Cheng-Chieh Cheng, Jeffrey P Guenette, Yajun Li, Bruno Madore, Lei Qin

Affiliation: Brigham and Women's Hospital, National Sun Yat-sen University, Dana-Farber Cancer Institute

Abstract Preview: Purpose: Pilot tone (PT), a compact RF sensor, has been integrated into clinical practice for motion detection. Prior studies proposed mapping PT signals to head positions using a calibration step tha...

Improving the Robustness of AI-Based Detection and Segmentation for Brain Metastasis By Optimizing Loss Function and Multi-Dataset Training

Authors: Omar Awad, Alfredo Enrique Echeverria, Issam M. El Naqa, Daniel Allan Hamstra, Yiding Han, Ryan Lafratta, Abdallah Sherif Radwan Mohamed, Piyush Pathak, Zaid Ali Siddiqui, Baozhou Sun, Vincent Ugarte

Affiliation: H. Lee Moffitt Cancer Center, Harris Health, Baylor College of Medicine

Abstract Preview: Purpose:
Accurate detection and segmentation of brain metastases are critical for diagnosis, treatment planning, and follow-up imaging but are challenging due to labor-intensive manual assessments ...

Incorporating Physicians’ Contouring Style into Auto-Segmentation of Clinical Target Volume for Post-Operative Prostate Cancer Radiotherapy Using a Language Encoder

Authors: Steve B. Jiang, Chien-Yi Liao, Dan Nguyen, Daniel Yang, Hengrui Zhao

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center

Abstract Preview: Purpose:
Post-operative radiotherapy for prostate cancer requires precise contouring of the clinical target volume (CTV) to account for microscopic disease that is invisible in the image. However, ...

Integrating Neuroanatomic Knowledge in Clinical Target Volumes for Glioma Patients Using Deep Learning

Authors: Ali Ajdari, Thomas R. Bortfeld, Christopher Bridge, Gregory Buti, Marcela Giovenco, Fredrik Lofman, Gregory C. Sharp, Helen A Shih, Tugba Yilmaz

Affiliation: Massachusetts General Hospital, RaySearch Laboratories, Department Of Radiation Oncology, Massachusetts General Hospital (MGH), Massachusetts General Hospital & Harvard Medical School, Massachusetts General Hospital and Harvard Medical School

Abstract Preview: Purpose: Defining radiation target volumes with accurate integration of the neuroanatomy is one of the major difficulties in designing glioma treatments. We developed a deep learning network for norma...

Inter-Patient Registration Methods for Voxel-Based Analysis in Lung Cancer

Authors: Chloe Min Seo Choi, Joseph O. Deasy, Jue Jiang, Sudharsan Madhavan, Nikhil Mankuzhy, Nishant Nadkarni, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu

Affiliation: University of Freibrug, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Voxel-based analysis (VBA) requires accurate topology-preserving inter-patient deformable image registration (DIR). This study assessed whether guiding a DIR method with geometric priors of t...

Investigating the Flash Effect: Uncovering Brain Sparing and Cognitive Preservation across Varying Dose Rates in Whole-Brain Irradiation of in-Vivo Mouse Models

Authors: Denise Dunn, Scott R. Floyd, Tyler V. Kay, Anna Lynnette-Price, Eric L. Martin, Stepan Mikhailov, Taylor Nguyen, Mark Oldham, Victoria J. P. Radosova, Zachary J. Reitman, Ramona Rodriguiz, Andrew Thompson, William Wetsel, Seth Wilcox, Ying Wu

Affiliation: Duke University Mouse Behavior Core, Department of Physics, Duke University, Medical Physics Graduate Program, Duke University, Duke University Medical Physics Program, Duke University, Duke University Mouse Behavioral and Neuroendocrine Analysis Core Facility, Department of Radiation Oncology, Duke University Medical Center

Abstract Preview: Purpose: FLASH radiation therapy (RT) shows promise for reducing normal tissue damage, though its mechanisms remain unclear. Using an in-vivo model with the High Intensity Gamma-ray Source (HIGS) lina...

Liver Tumor Auto-Contouring Using Recurrent Neural Networks on MRI-Linac for Adaptive Radiation Therapy

Authors: Yan Dai, Jie Deng, Christopher Kabat, Weiguo Lu, Ying Zhang, Hengrui Zhao

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center

Abstract Preview: Purpose:
MRI-guided adaptive radiotherapy (MRgART) using MR-LINAC systems offers significant advantages for liver cancer, enabling superior tumor delineation and online plan adaptation. However, ma...

Lymphocytic Feature Characterization Using a Deep Learning Algorithm on Post-Radiation Lymph Nodes

Authors: Rico Castillo, Katherine Gonzalez, Casey C. Heirman, Kyle J. Lafata, Casey Y. Lee, Xiang Li, Yvonne M Mowery, Yvonne M Mowery, Daniel Murphy, Allison Pittman, Ashlyn G. Rickard

Affiliation: Duke University, Department of Radiation Oncology, Duke University, University of Pittsburgh

Abstract Preview: Purpose: To evaluate the ability of a deep learning model to identify pathomic features in lymph nodes of preclinical head and neck squamous cell carcinoma (HNSCC) models as surrogates for predicting ...

MR-Based Functional Liver Imaging and Dosimetry to Predict Albi Change Post-SBRT

Authors: Madhava Aryal, James M. Balter, Yue Cao, Daniel T Chang, Kyle Cuneo, Joseph R. Evans, Theodore Lawrence, John Rice, Randall K. Ten Haken, Lise Wei

Affiliation: University of Michigan, Department of Radiation Oncology University of Michigan

Abstract Preview: Purpose: This study aims to identify predictors of global liver function change measured by albumin-bilirubin (ALBI) score following stereotactic body radiation therapy (SBRT) in hepatocellular carcin...

Multi-Modality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma

Authors: Tyler J Bradshaw, Sharon M Castellino, Steve Y Cho, David Hodgson, Bradford S Hoppe, Kara M Kelly, Andrea Lo, Sarah Milgrom, Xin Tie

Affiliation: Department of Radiation Oncology, University of Toronto, Department of Radiology, University of Wisconsin, University of Colorado Anschutz, Department of Medical Physics, University of Wisconsin, Department of Radiation Oncology, Mayo Clinic, Department of Radiation Oncology, BC Cancer, Vancouver Center, Department of Radiology, University of Wisconsin - Madison, Roswell Park Comprehensive Cancer Center, Emory University School of Medicine

Abstract Preview: Purpose: Clinical target volume (CTV) delineation for involved-site radiation therapy (ISRT) in Hodgkin lymphoma (HL) is time-consuming due to the need to analyze multi-time-point PET/CT scans co-regi...

Multi-Organ Segmentation of Pelvic Cone-Beam Computed Tomography (CBCT) with Transformer Models to Enhance Adaptive Radiotherapy for Prostate Cancer

Authors: Ming Chao, Thomas Chum, Tenzin Kunkyab, Yang Lei, Tian Liu, Richard G Stock, Hasan Wazir, Junyi Xia, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai

Abstract Preview: Purpose:
This study aims to develop effective strategies for multi-organ segmentation of pelvic cone-beam computed tomography (CBCT) images based on transformer models to facilitate adaptive radiat...

PCA-Based Future Frame Prediction for Real-Time MRI-Guided Radiotherapy

Authors: B. Gino Fallone, Gawon Han, Keith D. Wachowicz, Mark G. Wright, Eugene Yip, Jihyun Yun

Affiliation: Medical Physics Division, Department of Oncology, University of Alberta, Dept. of Medical Physics, Cross Cancer Institute and Dept. of Oncology, University of Alberta, Medical Physics Division, Department of Oncology, University of Alberta and Department of Medical Physics, Cross Cancer Institute, Dept. of Medical Physics, Cross Cancer Institute and Dept. of Oncology, University of Alberta; MagnetTx Oncology Solutions, www.magnetTX.com

Abstract Preview: Purpose: MRI-radiotherapy hybrid systems can guide the therapeutic beam, dynamically adjusting to a moving tumor in real-time. However, there is a time delay from imaging and beam control, requiring p...

Pancrea-Seg-Net: A Semi-Supervised Deep Learning Framework for Pancreatic Tumor and Vessel Segmentation

Authors: Manju Liu, Ning Wen, Fuhua Yan, Yanzhao Yang, Zhenyu Yang, Haoran Zhang, Lei Zhang, Yajiao Zhang

Affiliation: Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy where precise segmentation of tumors and adjacent vessels is crucial for effective treatment planning. This study dev...

Parameterized 4D Deformable Registration (p4Dreg) in Abdominal 4DCT Scans

Authors: Edward Robert Criscuolo, Deshan Yang

Affiliation: Duke University, Department of Radiation Oncology, Duke University

Abstract Preview: Purpose:
Deformable registration of 4DCT images has many clinical applications, but current methods are unreliable and can produce dangerous errors. Iterative, parametrized image registration does ...

Patient-Specific Imaging Modality Agnostic Virtual Digital Twins Modeling Temporally Varying Digestive Motion

Authors: James M. Balter, Lando S. Bosma, Jorge Tapias Gomez, Nishant Nadkarni, Mert R Sanbuncu, William Paul Segars, Ergys D. Subashi, Neelam Tyagi, Harini Veeraraghavan

Affiliation: University of Michigan, The University of Texas MD Anderson Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Cornell University, University Medical Center Utrecht, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Develop patient-specific virtual digital twin (VDT) cohorts modeling physically realistic spatio-temporal gastrointestinal (GI) organs (stomach and duodenum) digestive motion.
Methods: Pat...

Radiopathomic Characterization of Chemoradiation Resistance in Preclinical Models of Head and Neck Cancer

Authors: Rico Castillo, Katherine Gonzalez, Casey C. Heirman, Kyle J. Lafata, Xiang Li, Yvonne M Mowery, Yvonne M Mowery, Allison Pittman, Ashlyn G. Rickard, Breylon Riley

Affiliation: Duke University, Department of Radiation Oncology, Duke University, University of Pittsburgh

Abstract Preview: Purpose: To evaluate the relationships between quantitative imaging biomarkers and chemoradiation resistance in head and neck squamous cell carcinoma (HNSCC) using preclinical mouse models.

Met...

Ratoguide: Evaluation of AI-Driven Fully Automated Treatment Planning Support System for Lung SBRT

Authors: Keiichi Jingu, Noriyuki Kadoya, Takafumi Komiyama, Takeru Nakajima, Hikaru Nemoto, Hiroshi Onishi, Masahide Saito, Ryota Tozuka

Affiliation: Department of Radiology, University of Yamanashi, Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Department of Advanced Biomedical Imaging, University of Yamanashi

Abstract Preview: Purpose: We evaluated the accuracy of a new AI-based fully automated planning software in stereotactic body radiotherapy (SBRT) for early-stage lung cancer.
Methods: We collected data from 125 pati...

Redefining the Down-Sampling Scheme of U-Net for Precision Biomedical Image Segmentation

Authors: Yizheng Chen, Md Tauhidul Islam, Mingjie Li, Lei Xing

Affiliation: Department of Radiation Oncology, Stanford University

Abstract Preview: Purpose:
Biomedical image segmentation (BIS) is a cornerstone of medical physics, enabling accurate delineation of anatomical structures and abnormalities, which is critical for diagnosis, treatmen...

Refined Nnu-Net Training for Practice-Specific Autosegmentation of APBI Targets

Authors: Daniel A. Alexander, Jonathan Baron, Brook Kennedy Byrd, William Ross Green, Bolin Li, Rafe A. McBeth, Abigail Pepin, Steven Philbrook

Affiliation: Department of Radiation Oncology and Applied Sciences, Department of Radiation Oncology, University of Pennsylvania, Thayer School of Engineering, University of Pennsylvania

Abstract Preview: Purpose: As accelerated partial breast irradiation (APBI) gains traction, the prospect of a rapid sim-to-completion of treatment workflow is an attractive option for patients. While OAR autocontouring...

Retrospective Analysis of Shape and Dosage Changes in Structures during Radiotherapy for Head and Neck Cancer Patients Based on Velocity

Authors: Daming LI, Jinsen Xie, Zhe Zhang

Affiliation: Peking University Shenzhen Hospital Radiotherapy Department, School of Nuclear Science and Technology, University of South China

Abstract Preview: Purpose: To analyze the actual doses received during radiotherapy for head and neck cancers (HNC) using Velocity, providing insights for adaptive radiotherapy decision-making.
Methods: Thirty-three...

SPECT/CT Multimodal Segmentation of Bone Marrow for Theranostic Dosimetry

Authors: Tommaso Frigerio, Joshua Genender, John M. Hoffman, Catherine (Caffi) Meyer

Affiliation: UCLA, David Geffen School of Medicine at UCLA

Abstract Preview: Purpose: Accurate bone marrow segmentation is required for bone marrow dosimetry to monitor for dangers in PSMA-Lu177 radioligand therapy. We introduce a hybrid (AI/semantic knowledge) segmentation pi...

Small but Mighty: A Lightweight and Computationally Efficient Model for Deformable Image Registration

Authors: Hengjie Liu, Dan Ruan, Ke Sheng, DI Xu

Affiliation: Physics and Biology in Medicine, University of California, Los Angeles, Department of Radiation Oncology, University of California, San Francisco, Department of Radiation Oncology, University of California at San Francisco, Department of Radiation Oncology, University of California, Los Angeles

Abstract Preview: Purpose:
State-of-the-art deep learning-based deformable image registration often uses large, complex models directly adapted from computer vision tasks but achieves only comparable performance to ...

Spatially Informed Auto-Segmentation of Cardiac Nodes for Radiotherapy Treatment Planning

Authors: Ming Dong, Carri K. Glide-Hurst, Joshua Pan, Nicholas R. Summerfield

Affiliation: Department of Computer Science, Wayne State University, Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, Department of Human Oncology, University of Wisconsin-Madison

Abstract Preview: Purpose: Radiation dose to the cardiac nodes is more strongly associated with conduction disorders and arrythmias than whole heart (WH) metrics. However, node segmentation is challenging due to comple...

Structure-Based Diffusion Model for CT Synthesis from MR Images for Radiotherapy Treatment Planning

Authors: Samuel Kadoury, Redha Touati

Affiliation: Polytechnique Montréal

Abstract Preview: Purpose:
Generating synthetic CT images from MR acquisitions for radiotherapy planning allows to integrate soft tissue contrast alongside density information stemming from CT, thus improving tumor ...

Synthesizing High-Quality Hepatic Vascular Tree Segmentation Datasets to Improve Segmentation Model Performance

Authors: Trevor McKeown, Deshan Yang, Zhendong Zhang

Affiliation: Duke University, Department of Radiation Oncology, Duke University

Abstract Preview: Purpose: Accurate delineation of liver blood vascular structures is crucial for planning and executing therapeutic interventions in liver-related medical procedures. However, current auto-segmentation...

Towards AI Decision-Support for Online Adaptive Radiotherapy (oART): A Preliminary Study on CBCT-Guided Post-Prostatectomy Oart

Authors: Michael Cummings, Olga M. Dona Lemus, Hana Mekdash, Tyler Moran, Alexander R Podgorsak, Sean M. Tanny, Matthew J. Webster, Lexiang Yang, Dandan Zheng, Yuwei Zhou, Xiaofeng Zhu

Affiliation: Department of Radiation Oncology, University of Rochester, University of Miami, Inova Schar Cancer Institute, University of Rochester

Abstract Preview: Purpose: oART is revolutionizing radiotherapy by allowing treatment plans to be adjusted based on daily imaging, improving targeting precision and potentially enhancing patient outcomes. However, its ...