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SSCK-Net: Spine segmentation in MRI based on cross attention and key-points recognition-assisted learner

https://doi.org/10.1016/j.bspc.2023.105278
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-74113-0_10openalex-snapshot
Attention-Driven Deep Learning for Pathological Spine Segmentation
inferredcites →https://doi.org/10.1016/j.bspc.2022.104258openalex-snapshot
CPAD-Net: Contextual parallel attention and dilated network for liver tumor segmentation
inferredcites →https://doi.org/10.1016/j.bspc.2023.104622openalex-snapshot
CDA-Net: A contrastive deep adversarial model for prostate cancer segmentation in MRI images
inferredcites →https://doi.org/10.1016/j.compbiomed.2022.106190openalex-snapshot
A novel 3D lumbar vertebrae location and segmentation method based on the fusion envelope of 2D hybrid visual projection images
inferredcites →https://doi.org/10.1016/j.compmedimag.2023.102245openalex-snapshot
Semi-supervised hybrid spine network for segmentation of spine 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.2019.01.012openalex-snapshot
Attention gated networks: Learning to leverage salient regions in medical images
inferredcites →https://doi.org/10.1016/j.media.2021.102261openalex-snapshot
DGMSNet: Spine segmentation for MR image by a detection-guided mixed-supervised segmentation network
inferredcites →https://doi.org/10.1016/j.media.2023.102918openalex-snapshot
Segment anything model for medical image analysis: An experimental study
inferredcites →https://doi.org/10.1016/j.neuroimage.2006.01.015openalex-snapshot
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
inferredcites →https://doi.org/10.1016/j.neuroimage.2018.09.081openalex-snapshot
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
inferredcites →https://doi.org/10.1109/jbhi.2020.2969084openalex-snapshot
MRLN: Multi-Task Relational Learning Network for MRI Vertebral Localization, Identification, and Segmentation
inferredcites →https://doi.org/10.1109/tmi.2013.2268424openalex-snapshot
Lumbar Spine Segmentation Using a Statistical Multi-Vertebrae Anatomical Shape+Pose Model
inferredcites →https://doi.org/10.1109/tmi.2015.2437192openalex-snapshot
Automatic Segmentation of the Spinal Cord and Spinal Canal Coupled With Vertebral Labeling
inferredcites →https://doi.org/10.1109/tmi.2015.2443912openalex-snapshot
Statistical Interspace Models (SIMs): Application to Robust 3D Spine Segmentation
inferredcites →https://doi.org/10.1109/tmi.2019.2927289openalex-snapshot
Vertebrae Identification and Localization Utilizing Fully Convolutional Networks and a Hidden Markov Model
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/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.3390/app9030404openalex-snapshot
Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1506.04924openalex-snapshot
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1711.02257openalex-snapshot
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks

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.4629021openalex-extracted
Ca-Segresnet: A Context-Aware Segmentation Residual Network for Automatic Segmentation of Identified Vertebral Bones from Ct Images
inferred← citeshttps://doi.org/10.1007/s11547-025-01996-yopenalex-snapshot
Quantification and classification of lumbar disc herniation on axial magnetic resonance images using deep learning models
inferred← citeshttps://doi.org/10.1007/s44196-025-01036-7openalex-snapshot
MRI-Based Spinal Condition Classification Using Improved 3DRMAU-Net-DCSE and Ensembled Deep Learning with Optimization Techniques
inferred← citeshttps://doi.org/10.1038/s41598-024-79244-yopenalex-snapshot
WGAN-based multi-structure segmentation of vertebral cross-section MRI using ResU-Net and clustered transformer
inferred← citeshttps://doi.org/10.1088/2057-1976/ae21e5openalex-snapshot
Enhancing lumbar disc herniation classification through region-of-interest guidance and geometric shape features
inferred← citeshttps://doi.org/10.1109/cscwd64889.2025.11033260openalex-snapshot
EFONet: A Deep Learning-Based Method for Lumbar Disc Segmentation in Magnetic Resonance Images
inferred← citeshttps://doi.org/10.1177/08953996241299998openalex-snapshot
Spine X-ray image segmentation based on deep learning and marker controlled watershed
inferred← citeshttps://doi.org/10.1177/20552076251408517openalex-snapshot
DHAN: A deep hierarchical attention network for multisource biomedical data fusion in precision diabetic retinopathy diagnosis
inferred← citeshttps://doi.org/10.1186/s13018-024-05002-5openalex-snapshot
Deep learning assisted segmentation of the lumbar intervertebral disc: a systematic review and meta-analysis
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