AIMC Topic: Organs at Risk

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Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 8, 2020
PURPOSE: The delineation of the clinical target volume (CTV) is a crucial, laborious and subjective step in cervical cancer radiotherapy. The aim of this study was to propose and evaluate a novel end-to-end convolutional neural network (CNN) for full...
Organs at Risk Radiotherapy Planning, Computer-Assisted Uterine Cervical Neoplasms Deep Learning Humans Algorithms Female
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Complete abdomen and pelvis segmentation using U-net variant architecture.

Medical physics Oct 7, 2020
PURPOSE: Organ segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs-at-risk, which is laborious and time...
Humans Abdomen Pelvis Organs at Risk Neural Networks, Computer Tomography, X-Ray Computed Image Processing, Computer-Assisted
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Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Sep 28, 2020
Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer variation, giving rise to interests in auto-segmentation methods. We evaluated the feasibility of dee...
Humans Organs at Risk Tomography, X-Ray Computed Radiotherapy Planning, Computer-Assisted Breast Neoplasms Deep Learning
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Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning.

Physics in medicine and biology Sep 8, 2020
The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Field Fluence Prediction (AIP-SFFP), for automated prostate IMRT planning with real-time planning effi...
Humans Automation Radiotherapy Dosage Radiotherapy, Intensity-Modulated Prostatic Neoplasms Male Organs at Risk Deep Learning Radiotherapy Planning, Computer-Assisted
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Robustness study of noisy annotation in deep learning based medical image segmentation.

Physics in medicine and biology Aug 27, 2020
Partly due to the use of exhaustive-annotated data, deep networks have achieved impressive performance on medical image segmentation. Medical imaging data paired with noisy annotation are, however, ubiquitous, but little is known about the effect of ...
Signal-To-Noise Ratio Radiometry Head and Neck Neoplasms Organs at Risk Databases, Factual Tomography, X-Ray Computed Female Male Deep Learning Image Processing, Computer-Assisted Humans
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A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy.

Radiation oncology (London, England) Aug 3, 2020
BACKGROUND: To investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally advanced nasopharyngeal carcinoma (NPC) radiotherapy.
Radiotherapy, Intensity-Modulated Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Humans Organs at Risk Retrospective Studies Machine Learning Radiotherapy Planning, Computer-Assisted Knowledge Bases Radiotherapy Dosage
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Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy.

Medical physics Aug 2, 2020
PURPOSE: Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models for predicting Pareto optimal dose distributions have generated optimal plans in real time using an...
Radiotherapy Dosage Radiotherapy, Intensity-Modulated Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Male Deep Learning
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Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods.

Medical physics Jul 28, 2020
Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor c...
Head and Neck Neoplasms Organs at Risk Humans Radiotherapy Planning, Computer-Assisted Head Deep Learning
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Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy.

Practical radiation oncology Jun 27, 2020
PURPOSE: Auto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounti...
Rectum Organs at Risk Male Radiotherapy Planning, Computer-Assisted Humans Deep Learning Urinary Bladder Retrospective Studies Prostate
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Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples.

Acta oncologica (Stockholm, Sweden) Jun 22, 2020
Efficient and accurate methods are needed to automatically segmenting organs-at-risk (OAR) to accelerate the radiotherapy workflow and decrease the treatment wait time. We developed and evaluated the use of a fused model Dense V-Network for its abil...
Female Humans Uterine Cervical Neoplasms Deep Learning Workflow Neural Networks, Computer Organs at Risk Algorithms Spinal Cord Urinary Bladder Time-to-Treatment Intestines Femur Pelvis Rectum
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