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Results for "automatic breast": 7 found

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...

Accuracy Analysis of "Sphere-Mask" Optical Positioning System in Radiotherapy of Breast Cancer

Authors: Xiu tong Lin, Tao Sun

Affiliation: Department of Radiation physics and technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Department of Radiation physics and technology, Shandong Second Provincial General Hospital

Abstract Preview: Purpose: To compare the positioning accuracy and time efficiency of "Sphere-Mask" Optical Positioning System (OPS) and traditional laser light positioning method in breast cancer radiotherapy, and to ...

Automatic Breast VMAT Planning Using Script and Rapidplan on Eclipse

Authors: Maria Jose Almada, Bruno Forti, Patricia Murina, Carlos Daniel Venencia

Affiliation: Instituto Zunino - Fundacion Marie Curie, Dra.

Abstract Preview: Purpose: The use of VMAT for the breast requires duplication of the patient's CT, several planning structures, and patient-specific dose-volume constraints depending on the patient's anatomy, fraction...

From Prediction to Practice: Performance of a Deep Learning-Based Breast Planning Algorithm

Authors: Thomas L. Hayes, Nicholas C. Koch, Han Liu, Qingyang (Grace) Shang, Benjamin J. Sintay, Caroline Vanderstraeten, David B. Wiant

Affiliation: Fuse Oncology, Cone Health, Cone Health Cancer Center

Abstract Preview: Purpose:
This study evaluates the accuracy of a deep learning-based automatic breast planning script in predicting beam energy for breast cancer treatments. The script was validated and implemented...

Investigating Factors Impacting the Consistency of Signal-to-Noise Ratio in Mammographic Automatic Exposure Control

Authors: James Ward, Anzi Zhao

Affiliation: Northwestern Medicine

Abstract Preview: Purpose: This study investigates the impact of tissue-equivalent attenuator materials, the region of interest (ROI) size, and the orientation of the attenuator positioning on the measured signal-to-no...

Towards Real-Time Radiotherapy Monitoring By Cherenkov Imaging: Applications of Patient-Specific Bio-Morphological Features Segmented Via Deep Learning

Authors: Petr Bruza, Yao Chen, David J. Gladstone, Lesley A Jarvis, Brian W Pogue, Kimberley S Samkoe, Yucheng Tang, Shiru Wang, Rongxiao Zhang

Affiliation: NVIDIA Corp, Dartmouth College, Thayer School of Engineering, Dartmouth College, Dartmouth Cancer Center, University of Missouri, University of Wisconsin - Madison

Abstract Preview: Purpose: Cherenkov imaging provides real-time visualization of megavoltage radiation beam delivery during radiotherapy. Patient-specific bio-morphological features, such as vasculature, captured in th...

Validation of an Open Source Automatic Segmentation Tool for Personalized Dosimetry

Authors: Klaus Bacher, Louise D'hondt, Jeff Rutten, Gwenny Verfaillie

Affiliation: Ghent University

Abstract Preview: Purpose: Manual organ segmentation is a very time-consuming but necessary process in personalized dosimetry. Automatic segmentation tools may alleviate this task. In this study the impact of automatic...