Patch-shuffle-based semi-supervised segmentation of bone computed tomography via consistent learning
Bone segmentation is essential in Computed Tomography (CT) imaging, which assists
physicians in diagnosing, planning operations, and evaluating treatment effects. A recent …
physicians in diagnosing, planning operations, and evaluating treatment effects. A recent …
Semi-supervised segmentation of metastasis lesions in bone scan images
Q Lin, R Gao, M Luo, H Wang, Y Cao, Z Man… - Frontiers in Molecular …, 2022 - frontiersin.org
To develop a deep image segmentation model that automatically identifies and delineates
lesions of skeletal metastasis in bone scan images, facilitating clinical diagnosis of lung …
lesions of skeletal metastasis in bone scan images, facilitating clinical diagnosis of lung …
Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques
Background The purpose of this study was to develop and evaluate an algorithm for bone
segmentation on whole-body CT using a convolutional neural network (CNN). Methods …
segmentation on whole-body CT using a convolutional neural network (CNN). Methods …
Pairwise attention-enhanced adversarial model for automatic bone segmentation in CT images
Objective. Bone segmentation is a critical step in screw placement navigation. Although the
deep learning methods have promoted the rapid development for bone segmentation, the …
deep learning methods have promoted the rapid development for bone segmentation, the …
Automated bone tumor segmentation and classification as benign or malignant using computed tomographic imaging
The purpose of this study was to pair computed tomography (CT) imaging and machine
learning for automated bone tumor segmentation and classification to aid clinicians in …
learning for automated bone tumor segmentation and classification to aid clinicians in …
Medical image segmentation via unsupervised convolutional neural network
For the majority of the learning-based segmentation methods, a large quantity of high-quality
training data is required. In this paper, we present a novel learning-based segmentation …
training data is required. In this paper, we present a novel learning-based segmentation …
A bone segmentation method based on Multi-scale features fuse U2Net and improved dice loss in CT image process
T Liu, Y Lu, Y Zhang, J Hu, C Gao - Biomedical Signal Processing and …, 2022 - Elsevier
During the conventional CT image process, window width is set to extract target. However,
different tissues may have the same value of hounsfield unit in CT images, which cause …
different tissues may have the same value of hounsfield unit in CT images, which cause …
In-depth learning of automatic segmentation of shoulder joint magnetic resonance images based on convolutional neural networks
X Mu, Y Cui, R Bian, L Long, D Zhang, H Wang… - Computer Methods and …, 2021 - Elsevier
Objective Magnetic resonance imaging (MRI) is gradually replacing computed tomography
(CT) in the examination of bones and joints. The accurate and automatic segmentation of the …
(CT) in the examination of bones and joints. The accurate and automatic segmentation of the …
Cross-set data augmentation for semi-supervised medical image segmentation
Medical image semantic segmentation is a fundamental yet challenging research task.
However, training a fully supervised model for this task requires a substantial amount of …
However, training a fully supervised model for this task requires a substantial amount of …
CT‐based automatic spine segmentation using patch‐based deep learning
CT vertebral segmentation plays an essential role in various clinical applications, such as
computer‐assisted surgical interventions, assessment of spinal abnormalities, and vertebral …
computer‐assisted surgical interventions, assessment of spinal abnormalities, and vertebral …