Deep learning for segmentation in radiation therapy planning: a review
Segmentation of organs and structures, as either targets or organs‐at‐risk, has a significant
influence on the success of radiation therapy. Manual segmentation is a tedious and time …
influence on the success of radiation therapy. Manual segmentation is a tedious and time …
Artificial intelligence: reshaping the practice of radiological sciences in the 21st century
Advances in computing hardware and software platforms have led to the recent resurgence
in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for …
in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for …
Synthetic CT generation from CBCT images via deep learning
Purpose Cone‐beam computed tomography (CBCT) scanning is used daily or weekly (ie,
on‐treatment CBCT) for accurate patient setup in image‐guided radiotherapy. However …
on‐treatment CBCT) for accurate patient setup in image‐guided radiotherapy. However …
DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation
Deep learning algorithms have recently been developed that utilize patient anatomy and
raw imaging information to predict radiation dose, as a means to increase treatment …
raw imaging information to predict radiation dose, as a means to increase treatment …
A deep learning-based automated CT segmentation of prostate cancer anatomy for radiation therapy planning-a retrospective multicenter study
T Kiljunen, S Akram, J Niemelä, E Löyttyniemi… - Diagnostics, 2020 - mdpi.com
A commercial deep learning (DL)-based automated segmentation tool (AST) for computed
tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer …
tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer …
Automatic segmentation of pelvic cancers using deep learning: State-of-the-art approaches and challenges
R Kalantar, G Lin, JM Winfield, C Messiou… - Diagnostics, 2021 - mdpi.com
The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit
detail from large datasets have attracted substantial research attention in the field of medical …
detail from large datasets have attracted substantial research attention in the field of medical …
Attention-aware discrimination for MR-to-CT image translation using cycle-consistent generative adversarial networks
Purpose To suggest an attention-aware, cycle-consistent generative adversarial network (A-
CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to …
CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to …
A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy
A Balagopal, D Nguyen, H Morgan, Y Weng… - Medical image …, 2021 - Elsevier
In post-operative radiotherapy for prostate cancer, precisely contouring the clinical target
volume (CTV) to be irradiated is challenging, because the cancerous prostate gland has …
volume (CTV) to be irradiated is challenging, because the cancerous prostate gland has …
Deep learning in radiation oncology treatment planning for prostate cancer: a systematic review
G Almeida, JMRS Tavares - Journal of medical systems, 2020 - Springer
Radiation oncology for prostate cancer is important as it can decrease the morbidity and
mortality associated with this disease. Planning for this modality of treatment is both …
mortality associated with this disease. Planning for this modality of treatment is both …
Sequential vessel segmentation via deep channel attention network
Accurately segmenting contrast-filled vessels from X-ray coronary angiography (XCA) image
sequence is an essential step for the diagnosis and therapy of coronary artery disease …
sequence is an essential step for the diagnosis and therapy of coronary artery disease …