Radiological images and machine learning: trends, perspectives, and prospects

Z Zhang, E Sejdić - Computers in biology and medicine, 2019 - Elsevier
The application of machine learning to radiological images is an increasingly active
research area that is expected to grow in the next five to ten years. Recent advances in …

Automatic multi-organ segmentation on abdominal CT with dense V-networks

E Gibson, F Giganti, Y Hu, E Bonmati… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Automatic segmentation of abdominal anatomy on computed tomography (CT) images can
support diagnosis, treatment planning, and treatment delivery workflows. Segmentation …

Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction

X Fang, P Yan - IEEE Transactions on Medical Imaging, 2020 - ieeexplore.ieee.org
Shortage of fully annotated datasets has been a limiting factor in developing deep learning
based image segmentation algorithms and the problem becomes more pronounced in multi …

Marginal loss and exclusion loss for partially supervised multi-organ segmentation

G Shi, L Xiao, Y Chen, SK Zhou - Medical Image Analysis, 2021 - Elsevier
Annotating multiple organs in medical images is both costly and time-consuming; therefore,
existing multi-organ datasets with labels are often low in sample size and mostly partially …

Transfer learning for image segmentation by combining image weighting and kernel learning

A Van Opbroek, HC Achterberg… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Many medical image segmentation methods are based on the supervised classification of
voxels. Such methods generally perform well when provided with a training set that is …

Automatic organ segmentation for CT scans based on super-pixel and convolutional neural networks

X Liu, S Guo, B Yang, S Ma, H Zhang, J Li, C Sun… - Journal of digital …, 2018 - Springer
Accurate segmentation of specific organ from computed tomography (CT) scans is a basic
and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual …

Contextual atlas regression forests: multiple-atlas-based automated dose prediction in radiation therapy

C McIntosh, TG Purdie - IEEE transactions on medical imaging, 2015 - ieeexplore.ieee.org
Radiation therapy is an integral part of cancer treatment, but to date it remains highly
manual. Plans are created through optimization of dose volume objectives that specify intent …

Stratified decision forests for accurate anatomical landmark localization in cardiac images

O Oktay, W Bai, R Guerrero, M Rajchl… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
Accurate localization of anatomical landmarks is an important step in medical imaging, as it
provides useful prior information for subsequent image analysis and acquisition methods. It …

[PDF][PDF] Appearance-and context-sensitive features for brain tumor segmentation

R Meier, S Bauer, J Slotboom, R Wiest… - Proceedings of MICCAI …, 2014 - researchgate.net
The proposed method for fully-automatic brain tumor segmentation builds upon the
combined information from image appearance and image context. We employ a variety of …

A fusion of neural, genetic and ensemble machine learning approaches for enhancing the engineering predictive capabilities of lightweight foamed reinforced …

Y Chen, J Zeng, J Jia, M Jabli, N Abdullah, S Elattar… - Powder Technology, 2024 - Elsevier
This research explores lightweight foamed reinforced concrete beams, crucial in modern
construction for their strength and reduced weight. It introduces a novel approach …