0000000000403887

AUTHOR

Moona Mazher

showing 2 related works from this author

Automatic Segmentation Using a Hybrid Dense Network Integrated With an 3D-Atrous Spatial Pyramid Pooling Module for Computed Tomography (CT) Imaging

2020

Computed tomography (CT) with a contrast-enhanced imaging technique is extensively proposed for the assessment and segmentation of multiple organs, especially organs at risk. It is an important factor involved in the decision making in clinical applications. Automatic segmentation and extraction of abdominal organs, such as thoracic organs at risk, from CT images are challenging tasks due to the low contrast of pixel values surrounding other organs. Various deep learning models based on 2D and 3D convolutional neural networks have been proposed for the segmentation of medical images because of their automatic feature extraction capability based on large labeled datasets. In this paper, we p…

SegTHOR0209 industrial biotechnologyGeneral Computer ScienceComputer scienceFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyConvolutional neural network020901 industrial engineering & automationPyramid0202 electrical engineering electronic engineering information engineeringMedical imagingGeneral Materials ScienceSegmentationPyramid (image processing)3D deep learning modelsPixelbusiness.industryDeep learningGeneral EngineeringPattern recognition3D-atrous spatial pyramid pooling (ASPP)Feature (computer vision)3D volumetric segmentation020201 artificial intelligence & image processinglcsh:Electrical engineering. Electronics. Nuclear engineeringArtificial intelligencebusinesslcsh:TK1-9971IEEE Access
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Hybrid 3D-ResNet Deep Learning Model for Automatic Segmentation of Thoracic Organs at Risk in CT Images

2020

In image radiation therapy, accurate segmentation of organs at risk (OARs) is a very essential task and has clinical applications in cancer treatment. The segmentation of organs close to lung, breast, or esophageal cancer is a routine and time-consuming process. The automatic segmentation of organs at risk would be an essential part of treatment planning for patients suffering radiotherapy. The position and shape variation, morphology inherent and low soft tissue contrast between neighboring organs across each patient’s scans is the challenging task for automatic segmentation of OARs in Computed Tomography (CT) images. The objective of this paper is to use automatic segmentation of the orga…

business.industryComputer sciencemedicine.medical_treatmentDeep learningVolumetric segmentationPattern recognition02 engineering and technologyResidual neural network030218 nuclear medicine & medical imagingRadiation therapy03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineeringmedicineAutomatic segmentation020201 artificial intelligence & image processingSegmentationPyramid (image processing)Artificial intelligencebusinessRadiation treatment planning2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)
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