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Organs at Risk

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Advances in Auto-Segmentation.

Seminars in radiation oncology
Manual image segmentation is a time-consuming task routinely performed in radiotherapy to identify each patient's targets and anatomical structures. The efficacy and safety of the radiotherapy plan requires accurate segmentations as these regions of ...

The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.

Technology in cancer research & treatment
Radiotherapy is the main treatment strategy for nasopharyngeal carcinoma. A major factor affecting radiotherapy outcome is the accuracy of target delineation. Target delineation is time-consuming, and the results can vary depending on the experience ...

Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Medical physics
PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs ...

Automated fluence map optimization based on fuzzy inference systems.

Medical physics
PURPOSE: The planning of an intensity modulated radiation therapy treatment requires the optimization of the fluence intensities. The fluence map optimization (FMO) is many times based on a nonlinear continuous programming problem, being necessary fo...

Models for predicting objective function weights in prostate cancer IMRT.

Medical physics
PURPOSE: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate c...