AIMC Topic: Organs at Risk

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Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process.

Medical physics Feb 21, 2022
BACKGROUND: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process requires accurate and precise segmentation of the prostate and organs at risk (OARs), which is laborio...
Deep Learning Prostate Organs at Risk Artificial Intelligence Male Radiotherapy Planning, Computer-Assisted Humans
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Adopting Machine Learning Auto-Segmentation Into Radiotherapy Workflow.

Clinical oncology (Royal College of Radiologists (Great Britain)) Feb 17, 2022
Radiation Oncology Workflow Radiotherapy Planning, Computer-Assisted Organs at Risk Humans Machine Learning Image Processing, Computer-Assisted
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A hybrid optimization strategy for deliverable intensity-modulated radiotherapy plan generation using deep learning-based dose prediction.

Medical physics Feb 9, 2022
PURPOSE: To propose a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy, including dose prediction via a deep learning and voxel-based optimization strategy.
Humans Deep Learning Radiotherapy Dosage Radiotherapy, Intensity-Modulated Organs at Risk Nasopharyngeal Neoplasms Radiotherapy Planning, Computer-Assisted China
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Automatic contouring of normal tissues with deep learning for preclinical radiation studies.

Physics in medicine and biology Feb 9, 2022
Delineation of relevant normal tissues is a bottleneck in image-guided precision radiotherapy workflows for small animals. A deep learning (DL) model for automatic contouring using standardized 3D micro cone-beam CT (CBCT) volumes as input is propose...
Thorax Organs at Risk Radiotherapy Planning, Computer-Assisted Animals Lung Deep Learning Cone-Beam Computed Tomography Image Processing, Computer-Assisted
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General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis.

Medical physics Feb 7, 2022
PURPOSE: To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well-establi...
Male Deep Learning Image Processing, Computer-Assisted Humans Pelvis Organs at Risk Radiotherapy Planning, Computer-Assisted Abdomen Head and Neck Neoplasms
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Site-agnostic 3D dose distribution prediction with deep learning neural networks.

Medical physics Jan 27, 2022
PURPOSE: Typically, the current dose prediction models are limited to small amounts of data and require retraining for a specific site, often leading to suboptimal performance. We propose a site-agnostic, three-dimensional dose distribution predictio...
Humans Radiotherapy Planning, Computer-Assisted Deep Learning Neural Networks, Computer Radiotherapy Dosage Organs at Risk Male Radiotherapy, Intensity-Modulated
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Qualitative Evaluation of Common Quantitative Metrics for Clinical Acceptance of Automatic Segmentation: a Case Study on Heart Contouring from CT Images by Deep Learning Algorithms.

Journal of digital imaging Jan 26, 2022
Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep lear...
Algorithms Tomography, X-Ray Computed Deep Learning Benchmarking Humans Organs at Risk
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Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT.

Physics in medicine and biology Jan 17, 2022
Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process.CT scans of 242 head and neck ...
Deglutition Mastication Reproducibility of Results Deep Learning Humans Organs at Risk Retrospective Studies Head and Neck Neoplasms Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed
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Machine Learning for Auto-Segmentation in Radiotherapy Planning.

Clinical oncology (Royal College of Radiologists (Great Britain)) Jan 5, 2022
Manual segmentation of target structures and organs at risk is a crucial step in the radiotherapy workflow. It has the disadvantages that it can require several hours of clinician time per patient and is prone to inter- and intra-observer variability...
Humans Machine Learning Image Processing, Computer-Assisted Organs at Risk Deep Learning Radiotherapy Planning, Computer-Assisted Observer Variation
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A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer.

Scientific reports Nov 26, 2021
Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large ...
Lung Neoplasms Rectal Neoplasms Radiotherapy, Intensity-Modulated Tomography, X-Ray Computed Radiotherapy Planning, Computer-Assisted Humans Deep Learning Organs at Risk Radiotherapy Dosage Software Image Processing, Computer-Assisted
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