QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: A human skull phantom study
The purpose of this study was to directly and quantitatively measure BMD from Cone-beam
CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based …
CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based …
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 …
[HTML][HTML] Charting the potential of brain computed tomography deep learning systems
Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology.
The implementation and adoption of CTB has led to clinical improvements. However …
The implementation and adoption of CTB has led to clinical improvements. However …
Cone‐beam CT‐derived relative stopping power map generation via deep learning for proton radiotherapy
Purpose In intensity‐modulated proton therapy (IMPT), protons are used to deliver highly
conformal dose distributions, targeting tumors, and sparing organs‐at‐risk. However, due to …
conformal dose distributions, targeting tumors, and sparing organs‐at‐risk. However, due to …
Artificial intelligence in tumor subregion analysis based on medical imaging: A review
Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial
intelligence (AI) has achieved tremendous success in medical image analysis. This paper …
intelligence (AI) has achieved tremendous success in medical image analysis. This paper …
CBCT-based synthetic MRI generation for CBCT-guided adaptive radiotherapy
Cone-beam computed tomography (CBCT) has been widely used in image-guided radiation
therapy for patient setup to improve treatment performance. However, the low soft tissue …
therapy for patient setup to improve treatment performance. However, the low soft tissue …
Cone beam CT (CBCT) based synthetic CT generation using deep learning methods for dose calculation of nasopharyngeal carcinoma radiotherapy
X Xue, Y Ding, J Shi, X Hao, X Li, D Li… - … in Cancer Research …, 2021 - journals.sagepub.com
Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning
CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients …
CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients …
Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy
Objective. Adaptive radiotherapy workflows require images with the quality of computed
tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we …
tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we …
Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-GAN
MRI-only treatment planning is highly desirable in the current proton radiation therapy
workflow due to its appealing advantages such as bypassing MR-CT co-registration …
workflow due to its appealing advantages such as bypassing MR-CT co-registration …
Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with cone-beam computed tomography (CBCT) to computed …
Generalizability is a concern when applying a deep learning (DL) model trained on one
dataset to other datasets. It is challenging to demonstrate a DL model's generalizability …
dataset to other datasets. It is challenging to demonstrate a DL model's generalizability …