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Results for "hd95 dice": 22 found

A Tumor Tracking Method in Surface-Guided Radiotherapy

Authors: Penghao Gao, Zejun Jiang

Affiliation: Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Artificial Intelligence Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences

Abstract Preview: Purpose: Real-time tumor tracking can effectively compensate for the impact of respiratory motion on dose distribution. We propose a patient-specific external-internal correlation model driven by opti...

An Efficient Deep Learning Model with Multi-Scale Integration for Automated Pancreas Segmentation on MR Images

Authors: Jingyun Chen, Yading Yuan

Affiliation: Columbia University Irving Medical Center, Department of Radiation Oncology

Abstract Preview: Purpose: To develop and evaluate the Scale-attention network (SANet) for automated pancreas segmentation on MR images.
Methods: To develop SANet, we extended the classic U-Net design with a dynamic...

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, ...

Comprehensive Evaluation of Federated Learning Strategies for Head and Neck Tumor Segmentation on PET/CT Images

Authors: Jingyun Chen, Yading Yuan

Affiliation: Columbia University Irving Medical Center, Department of Radiation Oncology

Abstract Preview: Purpose: To evaluate centralized and decentralized strategies for federated head and neck tumor segmentation on PET/CT.
Methods: We utilized training data from the HEad and neCK TumOR segmentation ...

Deep Learning-Driven Comparative Analysis of CNN-Based Architectures and High-Order Vision Mamba U-Net (H-vMUNet) for MRI-Based Brain Tumor Segmentation

Authors: Sang Hee Ahn, Nalee Kim, Do Hoon Lim

Affiliation: Samsung Medical Center, Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine

Abstract Preview: Purpose: MRI offers superior soft-tissue contrast, aiding tumor localization and segmentation in radiation therapy, which traditionally relies on oncologists' expertise. This study compares CNN-based ...

Development and Validation of a Deep Learning-Based Auto-Segmentation Module for Vestibular Schwannoma

Authors: John Byun, Steven D Chang, Cynthia Fu-Yu Chuang, Xuejun Gu, Melanie Hayden Gephart, Yusuke Hori, Fred Lam, Gordon Li, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang, Xianghua Ye, Kangning Zhang

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Department of Neurosurgery, Stanford University, Department of Radiation Oncology, Stanford University, Department of Radiation Oncology, Stanford University School of Medicine

Abstract Preview: Purpose: Accurate and automated delineation of vestibular schwannoma (VS) volume is crucial for disease management, as both treatment approaches (stereotactic radiosurgery and invasive surgery) and mo...

Enhanced Pelvic Organ Segmentation Using LLM-Driven Prompts for Prostate Cancer Low-Dose-Rate Brachytherapy

Authors: Yang Lei, Tian Liu, Ren-Dih Sheu, Meysam Tavakoli, Jing Wang, Kaida Yang, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai, Department of Radiation Oncology, Emory University

Abstract Preview: Purpose:
The study aimed to improve target and organ at risk (OAR) segmentation in low-dose-rate brachytherapy (LDR-BT) for prostate cancer treatment, by integrating clinical guidelines into deep l...

Enhancing Adaptive Radiotherapy Segmentation with a 3D Unet Framework and Prior Fraction Information

Authors: Jennifer L. Dolan, Chengyin Li, Parag Parikh, Doris N. Rusu, Kundan S Thind

Affiliation: Henry Ford Health, Cedars-Sinai Medical Center

Abstract Preview: Purpose: The time and resource demands of online Adaptive Radiation Therapy (ART) can limit its widespread clinical adoption and potentially impact patient throughput. To address this, we developed a ...

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...

Foundation Model-Augmented Learning for Automatic Delineation in Precision Radiotherapy

Authors: Xianjin Dai, PhD, Michael Gensheimer, Praveenbalaji Rajendran, Lei Xing, Yong Yang

Affiliation: Department of Radiation Oncology, Stanford University, Massachusetts General Hospital, Harvard Medical School

Abstract Preview: Purpose: Recent advances in the automatic delineation of radiotherapy treatment targets, which incorporate linguistic clinical data extracted by large language models (LLMs) into traditional visual-on...

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...

Modality-Agnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation

Authors: Ming Dong, Carri K. Glide-Hurst, Qisheng He, Anudeep Kumar, Alex Singleton Kuo, Joshua Pan, Chase Ruff, 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: Recent evidence highlights the importance of incorporating cardiac substructures (CS) into treatment planning for thoracic cancers, however current segmentation methods are limited to a singl...

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...

NA-Unetr: A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery

Authors: Hassan Bagher-Ebadian, Ahmed I Ghanem, Joshua P. Kim, Chengyin Li, Rafi Ibn Sultan, Kundan S Thind, Dongxiao Zhu

Affiliation: Wayne State University, Department of Radiation Oncology, Henry Ford Health-Cancer, Detroit, MI and Alexandria Department of Clinical Oncology, Faculty of Medicine, Alexandria University, Henry Ford Health

Abstract Preview: Purpose: Accurate segmentation of the Left Anterior Descending (LAD) artery in free-breathing 3D treatment planning CT is crucial for radiotherapy but remains challenging due to its small size, comple...

Nnae: Automating Anomaly Detection and Quality Assurance in Medical Image Segmentation

Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang

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

Abstract Preview: Purpose:
Deep learning-based automatic medical image segmentation is increasingly employed in clinical practice, significantly reducing manual workload. However, verifying segmentation results rema...

Optimizing Atlas Counts for MRI-Guided Atlas-Based Autosegmentation of Swallowing Muscles in Head and Neck Radiotherapy

Authors: Zayne Belal, Rachel Drummey, Clifton David Fuller, Stephen Y. Lai, Brigid A. McDonald, Setareh Sharafi, Sonja Stieb, Kareem Abdul Wahid

Affiliation: Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Hospital of the University of Pennsylvania, Department of Radiology, Johns Hopkins University, KSA-KSB, Cantonal Hospital Aarau, College Of Osteopathic Medicine, NOVA Southeastern University, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose:
Radiotherapy-induced dysphagia can significantly impair head and neck (H&N) cancer patients’ quality of life. Despite the dose-dependent relationship between radiotherapy and dysphagia, sw...

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...

Quality Monitoring of Temporal Performance Degradation in Clinical Use of AI Auto-Segmentation

Authors: Ali Ammar, Quan Chen, Jingwei Duan, Yi Rong, Nathan Y. Yu, Libing Zhu

Affiliation: Mayo Clinic Arizona, University of Alabama at Birmingham

Abstract Preview: Purpose: Clinical performance of deep learning-based auto-segmentation (DLAS) can degrade over time due to AI β€œaging” from unseen data input compared to the initial model training data. This study aim...

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...

Uncertainty-Guided Cross-Domain Adaptation for Unsupervised Medical Image Segmentation

Authors: Yunxiang Li, Weiguo Lu, Xiaoxue Qian, Hua-Chieh Shao, 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

Abstract Preview: Purpose:
Curating high-quality, labeled data for medical image segmentation can be challenging and costly, considering the existence of various image domains with differing modalities/protocols. Cr...

Weakly Supervised Spatial Implicit Neural Representation Learning for 3D MRI-Ultrasound Deformable Image Registration in HDR Prostate Brachytherapy

Authors: Michael Baine, Yang Lei, Yu Lei, Ruirui Liu, Tian Liu, Jing Wang

Affiliation: Icahn School of Medicine at Mount Sinai, University of Nebraska Medical Center

Abstract Preview: Purpose: Accurate 3D deformable registration of MRI and ultrasound (US) is essential for real-time image guidance during high-dose-rate (HDR) prostate brachytherapy. However, MRI-US registration of th...