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Found 11 papers, 2 papers with code
Deep Learning in Medical Image Registration: A Review
Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.
Machine Learning in Quantitative PET Imaging
This paper reviewed the machine learning-based studies for quantitative positron emission tomography (PET).
Deep Learning in Multi-organ Segmentation
This paper presents a review of deep learning (DL) in multi-organ segmentation.
Synthetic MRI-aided Head-and-Neck Organs-at-Risk Auto-Delineation for CBCT-guided Adaptive Radiotherapy
The proposed method was evaluated on a cohort of 65 HN cancer patients.
Medical Physics Image and Video Processing
Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review
We summarize the latest AI-based methods for tumor subregion analysis and their applications.
Deformable Image Registration using Unsupervised Deep Learning for CBCT-guided Abdominal Radiotherapy
The STN consists of a global generative adversarial network (GlobalGAN) and a local GAN (LocalGAN) to predict the coarse- and fine-scale motions, respectively.
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation
In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR) in abdominal Computed Tomography (CT) to facilitate radiation therapy.
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis
The two DDPMs exchange random latent noise in the reverse processes, which helps to regularize both DDPMs and generate matching images in two modalities.
2D medical image synthesis using transformer-based denoising diffusion probabilistic model
In this paper, we introduce a medical image synthesis framework aimed at addressing the challenge of limited training datasets for AI models.
Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model
The proposed model consists of two processes: a forward process which adds Gaussian noise to real CT scans, and a reverse process in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans.
Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model
One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure.
