Radiological images and machine learning: trends, perspectives, and prospects
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 …
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
Automatic segmentation of abdominal anatomy on computed tomography (CT) images can
support diagnosis, treatment planning, and treatment delivery workflows. Segmentation …
support diagnosis, treatment planning, and treatment delivery workflows. Segmentation …
Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction
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 …
based image segmentation algorithms and the problem becomes more pronounced in multi …
Marginal loss and exclusion loss for partially supervised multi-organ segmentation
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 …
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 …
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 …
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 …
manual. Plans are created through optimization of dose volume objectives that specify intent …
Stratified decision forests for accurate anatomical landmark localization in cardiac images
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 …
provides useful prior information for subsequent image analysis and acquisition methods. It …
[PDF][PDF] Appearance-and context-sensitive features for brain tumor segmentation
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 …
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 …
This research explores lightweight foamed reinforced concrete beams, crucial in modern
construction for their strength and reduced weight. It introduces a novel approach …
construction for their strength and reduced weight. It introduces a novel approach …