CPFNet: Context pyramid fusion network for medical image segmentation
Accurate and automatic segmentation of medical images is a crucial step for clinical
diagnosis and analysis. The convolutional neural network (CNN) approaches based on the …
diagnosis and analysis. The convolutional neural network (CNN) approaches based on the …
A systematic review of the techniques for the automatic segmentation of organs-at-risk in thoracic computed tomography images
M Ashok, A Gupta - Archives of Computational Methods in Engineering, 2021 - Springer
The standard treatment for the cancer is the radiotherapy where the organs nearby the target
volumes get affected during treatment called the Organs-at-risk. Segmentation of Organs-at …
volumes get affected during treatment called the Organs-at-risk. Segmentation of Organs-at …
A statistical deformation model-based data augmentation method for volumetric medical image segmentation
The accurate delineation of organs-at-risk (OARs) is a crucial step in treatment planning
during radiotherapy, as it minimizes the potential adverse effects of radiation on surrounding …
during radiotherapy, as it minimizes the potential adverse effects of radiation on surrounding …
Deep learning on multiphysical features and hemodynamic modeling for abdominal aortic aneurysm growth prediction
Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the
early treatment and surgical intervention of AAA. Capturing key features of vascular growth …
early treatment and surgical intervention of AAA. Capturing key features of vascular growth …
Hybrid 3D-ResNet deep learning model for automatic segmentation of thoracic organs at risk in CT images
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 …
essential task and has clinical applications in cancer treatment. The segmentation of organs …
Automatic segmentation using a hybrid dense network integrated with an 3D-atrous spatial pyramid pooling module for computed tomography (CT) imaging
Computed tomography (CT) with a contrast-enhanced imaging technique is extensively
proposed for the assessment and segmentation of multiple organs, especially organs at risk …
proposed for the assessment and segmentation of multiple organs, especially organs at risk …
Automatic segmentation of organs-at-Risk in thoracic computed tomography images using ensembled U-net InceptionV3 model
M Ashok, A Gupta - Journal of Computational Biology, 2023 - liebertpub.com
The objective of this article is to automatically segment organs at risk (OARs) for thoracic
radiology in computed tomography (CT) scan images. The OARs in the thoracic anatomical …
radiology in computed tomography (CT) scan images. The OARs in the thoracic anatomical …
Improved organs at risk segmentation based on modified U‐Net with self‐attention and consistency regularisation
Cancer is one of the leading causes of death in the world, with radiotherapy as one of the
treatment options. Radiotherapy planning starts with delineating the affected area from …
treatment options. Radiotherapy planning starts with delineating the affected area from …
A cascaded FAS-UNet+ framework with iterative optimization strategy for segmentation of organs at risk
H Zhu, S Shu, J Zhang - Medical & Biological Engineering & Computing, 2024 - Springer
Segmentation of organs at risks (OARs) in the thorax plays a critical role in radiation therapy
for lung and esophageal cancer. Although automatic segmentation of OARs has been …
for lung and esophageal cancer. Although automatic segmentation of OARs has been …
ContourGAN: Auto‐contouring of organs at risk in abdomen computed tomography images using generative adversarial network
Accurately identifying and contouring the organs at risk (OARs) is a crucial step in radiation
treatment planning for precise dose calculation. This task becomes especially challenging in …
treatment planning for precise dose calculation. This task becomes especially challenging in …