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A review of deep learning based methods for medical image multi-organ segmentation
Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …
performance in many medical image segmentation tasks. Many deep learning-based …
Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods
The rapid expansion of machine learning is offering a new wave of opportunities for nuclear
medicine. This paper reviews applications of machine learning for the study of attenuation …
medicine. This paper reviews applications of machine learning for the study of attenuation …
CBCT‐based synthetic CT generation using deep‐attention cycleGAN for pancreatic adaptive radiotherapy
Purpose Current clinical application of cone‐beam CT (CBCT) is limited to patient setup.
Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT‐based …
Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT‐based …
A review on progress in semantic image segmentation and its application to medical images
Semantic image segmentation is a popular image segmentation technique where each pixel
in an image is labeled with an object class. This technique has become a vital part of image …
in an image is labeled with an object class. This technique has become a vital part of image …
Adversarial deep learning for improved abdominal organ segmentation in CT scans
Abdominal systems such the liver, pancreas, spleen, and kidneys must be carefully
dissected in order to properly diagnose and treat abdominal illnesses. Even while deep …
dissected in order to properly diagnose and treat abdominal illnesses. Even while deep …
CT‐based multi‐organ segmentation using a 3D self‐attention U‐net network for pancreatic radiotherapy
Purpose Segmentation of organs‐at‐risk (OARs) is a weak link in radiotherapeutic treatment
planning process because the manual contouring action is labor‐intensive and time …
planning process because the manual contouring action is labor‐intensive and time …
A systematic review of automated segmentation methods and public datasets for the lung and its lobes and findings on computed tomography images
Objectives: Automated computational segmentation of the lung and its lobes and findings in
X-Ray based computed tomography (CT) images is a challenging problem with important …
X-Ray based computed tomography (CT) images is a challenging problem with important …
Deep learning in multi-organ segmentation
This paper presents a review of deep learning (DL) in multi-organ segmentation. We
summarized the latest DL-based methods for medical image segmentation and applications …
summarized the latest DL-based methods for medical image segmentation and applications …
Toothpix: Pixel-level tooth segmentation in panoramic x-ray images based on generative adversarial networks
Accurate tooth segmentation in panoramic X-ray images is an essential stage before clinical
surgery. This paper presents a deep segmentation network ToothPix, which leverages …
surgery. This paper presents a deep segmentation network ToothPix, which leverages …
Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net
G Zhang, Z Yang, B Huo, S Chai, S Jiang - Computer methods and …, 2021 - Elsevier
Abstract Background and Objective Accurately and reliably defining organs at risk (OARs)
and tumors are the cornerstone of radiation therapy (RT) treatment planning for lung cancer …
and tumors are the cornerstone of radiation therapy (RT) treatment planning for lung cancer …