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Semi-supervised hybrid spine network for segmentation of spine MR images

https://doi.org/10.1016/j.compmedimag.2023.102245
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.bspc.2018.08.017openalex-snapshot
MRI and PET/SPECT image fusion at feature level using ant colony based segmentation
inferredcites →https://doi.org/10.1016/j.compbiomed.2021.104836openalex-snapshot
TA-Net: Triple attention network for medical image segmentation
inferredcites →https://doi.org/10.1016/j.compmedimag.2021.101872openalex-snapshot
Vestibule segmentation from CT images with integration of multiple deep feature fusion strategies
inferredcites →https://doi.org/10.1016/j.compmedimag.2022.102092openalex-snapshot
A contrastive consistency semi-supervised left atrium segmentation model
inferredcites →https://doi.org/10.1016/j.compmedimag.2022.102173openalex-snapshot
Residual attention based uncertainty-guided mean teacher model for semi-supervised breast masses segmentation in 2D ultrasonography
inferredcites →https://doi.org/10.1016/j.knosys.2021.106754openalex-snapshot
Multiscale fused network with additive channel–spatial attention for image segmentation
inferredcites →https://doi.org/10.1016/j.media.2018.01.004openalex-snapshot
3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images
inferredcites →https://doi.org/10.1016/j.media.2018.08.005openalex-snapshot
Spine-GAN: Semantic segmentation of multiple spinal structures
inferredcites →https://doi.org/10.1016/j.media.2019.02.005openalex-snapshot
Iterative fully convolutional neural networks for automatic vertebra segmentation and identification
inferredcites →https://doi.org/10.1016/j.media.2020.101861openalex-snapshot
Sequential conditional reinforcement learning for simultaneous vertebral body detection and segmentation with modeling the spine anatomy
inferredcites →https://doi.org/10.1016/j.media.2021.102258openalex-snapshot
Spine-transformers: Vertebra labeling and segmentation in arbitrary field-of-view spine CTs via 3D transformers
inferredcites →https://doi.org/10.1038/s41592-020-01008-zopenalex-snapshot
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
inferredcites →https://doi.org/10.1088/0031-9155/56/23/011openalex-snapshot
Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images
inferredcites →https://doi.org/10.1088/0031-9155/57/24/8357openalex-snapshot
Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models
inferredcites →https://doi.org/10.1088/1757-899x/803/1/012004openalex-snapshot
Deep Residual U-Net Based Lung Image Segmentation for Lung Disease Detection
inferredcites →https://doi.org/10.1109/access.2020.3025372openalex-snapshot
MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation
inferredcites →https://doi.org/10.1109/cvpr12618.2008openalex-snapshot
2008 IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr40276.2018openalex-snapshot
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00421openalex-snapshot
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
inferredcites →https://doi.org/10.1109/iccv.2019.00683openalex-snapshot
Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning
inferredcites →https://doi.org/10.1109/iccv19984.2013openalex-snapshot
2013 IEEE International Conference on Computer Vision
inferredcites →https://doi.org/10.1109/tmi.2015.2403285openalex-snapshot
Localization and Segmentation of 3D Intervertebral Discs in MR Images by Data Driven Estimation
inferredcites →https://doi.org/10.1109/tmi.2020.3025087openalex-snapshot
SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework With Semantic Image Representation
inferredcites →https://doi.org/10.1109/tmi.2024.3398728openalex-snapshot
UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation
inferredcites →https://doi.org/10.1109/tnnls.2020.2995319openalex-snapshot
Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation
inferredcites →https://doi.org/10.3390/diagnostics11030501openalex-snapshot
ASCU-Net: Attention Gate, Spatial and Channel Attention U-Net for Skin Lesion Segmentation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4482074openalex-extracted
inferred← citeshttps://doi.org/10.1002/ima.70280openalex-snapshot
SpineDeep ‐Net: Dual‐Self‐Attention‐Based Deep Neural Network for Automating Slice Selection and Precise Transverse Plane Localization in Lumbar Spine MRI for Intervertebral Disc Analysis
inferred← citeshttps://doi.org/10.1007/978-981-95-5631-1_3openalex-snapshot
Advancing Fine-Grained Spine Segmentation Through Visual-Language Model with Omni- and Pixel-Level Semantic Enhancements
inferred← citeshttps://doi.org/10.1007/978-981-95-6730-0_11openalex-snapshot
Deep Learning-Based Quantification of Lumbar Disc Herniation on High-Resolution Magnetic Resonance Imaging
inferred← citeshttps://doi.org/10.1007/978-981-97-6992-6_33openalex-snapshot
Deep Learning-Based Automatic Segmentation of Spinal Magnetic Resonance Images
inferred← citeshttps://doi.org/10.1007/s10278-023-00931-9openalex-snapshot
The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations
inferred← citeshttps://doi.org/10.1007/s10586-025-05122-5openalex-snapshot
Spinal segmentation based on heatmap regression positioning and improved UNet
inferred← citeshttps://doi.org/10.1007/s11263-026-02852-wopenalex-snapshot
Anatomy-Aware Text-Visual Fusion with Dual-Perspective Prompts for Fine-Grained Lumbar Spine Segmentation
inferred← citeshttps://doi.org/10.1007/s11548-024-03219-7openalex-snapshot
An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging
inferred← citeshttps://doi.org/10.1007/s11760-024-03548-9openalex-snapshot
Spinal MRI image segmentation based on improved U-Net
inferred← citeshttps://doi.org/10.1007/s11760-025-04531-8openalex-snapshot
Multi-view feature extraction encoder based on U-Net and linear transformer for adolescent idiopathic scoliosis spine segmentation
inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105278openalex-snapshot
SSCK-Net: Spine segmentation in MRI based on cross attention and key-points recognition-assisted learner
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108374openalex-snapshot
SDMI-Net: Spatially Dependent Mutual Information Network for semi-supervised medical image segmentation
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108430openalex-snapshot
On the use of contrastive learning for standard-plane classification in fetal ultrasound imaging
inferred← citeshttps://doi.org/10.1016/j.compmedimag.2026.102780openalex-snapshot
CGHNet: Cross-Guided 2D–3D Hybrid Network with attention mechanism for focal liver lesion classification
inferred← citeshttps://doi.org/10.1016/j.media.2024.103380openalex-snapshot
Semantics and instance interactive learning for labeling and segmentation of vertebrae in CT images
inferred← citeshttps://doi.org/10.1016/j.spinee.2025.05.003openalex-snapshot
Development of a novel machine learning model to automate vertebral column segmentation utilizing biplanar full-body imaging
inferred← citeshttps://doi.org/10.1016/j.xnsj.2025.100598openalex-snapshot
Integrating finite element analysis and physics-informed neural networks for biomechanical modeling of the human lumbar spine
inferred← citeshttps://doi.org/10.1038/s41598-025-29112-0openalex-snapshot
Enhancing breast magnetic resonance imaging segmentation with a federated semi-supervised approach
inferred← citeshttps://doi.org/10.1080/0954898x.2024.2323530openalex-snapshot
A Spinal MRI Image Segmentation Method Based on Improved Swin-UNet
inferred← citeshttps://doi.org/10.1097/br9.0000000000000005openalex-snapshot
Artificial intelligence in spine surgery
inferred← citeshttps://doi.org/10.1109/acroset62108.2024.10743738openalex-snapshot
Comprehensive Analysis of Deep Learning in Spine Image Segmentation
inferred← citeshttps://doi.org/10.1109/embc53108.2024.10781902openalex-snapshot
A Three-Stage Semi-Supervised Learning Approach to Spine Image Segmentation
inferred← citeshttps://doi.org/10.1109/icsps66615.2025.11347614openalex-snapshot
A Study on Breast Imaging Reporting and Data System Classification Based on YOLOv8
inferred← citeshttps://doi.org/10.1109/jbhi.2026.3678728openalex-snapshot
Wavelet-Driven Spatial Frequency Mamba Network for Spine Image Segmentation
inferred← citeshttps://doi.org/10.1186/s12938-025-01341-4openalex-snapshot
WDRIV-Net: a weighted ensemble transfer learning to improve automatic type stratification of lumbar intervertebral disc bulge, prolapse, and herniation
inferred← citeshttps://doi.org/10.3390/bioengineering11020164openalex-snapshot
Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion
inferred← citeshttps://doi.org/10.3390/bioengineering11101031openalex-snapshot
Verdiff-Net: A Conditional Diffusion Framework for Spinal Medical Image Segmentation
inferred← citeshttps://doi.org/10.52589/bjcnit-mizpefhdopenalex-snapshot
From Black Box to Clinical Trust: A Conceptual Review of Explainable and Lightweight Deep Learning for Spine Disease Detection and Segmentation
inferred← citeshttps://doi.org/10.53469/wjimt.2025.08(02).05openalex-snapshot
SCAR-Net: Spine Segmentation in MRI based on Cross Attention and Recognition-assisted Label Fusion
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