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Ca-Segresnet: A Context-Aware Segmentation Residual Network for Automatic Segmentation of Identified Vertebral Bones from Ct Images

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

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CBAM: Convolutional Block Attention Module

inferredcites →https://doi.org/10.1007/978-3-030-01261-8_1openalex-extracted

Group Normalization

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3D MRI Brain Tumor Segmentation Using Autoencoder Regularization

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A Robust Segmentation Framework for Spine Trauma Diagnosis

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Atlas-Based Registration for Accurate Segmentation of Thoracic and Lumbar Vertebrae in CT Data

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U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1007/978-3-319-41827-8_7openalex-extracted

Multi-atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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A Region-Based Deep Level Set Formulation for Vertebral Bone Segmentation of Osteoporotic Fractures

inferredcites →https://doi.org/10.1016/j.bspc.2021.103172openalex-extracted

Autonomous localization and segmentation for body composition quantization on abdominal CT

inferredcites →https://doi.org/10.1016/j.bspc.2023.105153openalex-extracted

3D MRU-Net: A novel mobile residual U-Net deep learning model for spine segmentation using computed tomography images

inferredcites →https://doi.org/10.1016/j.bspc.2023.105278openalex-extracted

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

inferredcites →https://doi.org/10.1038/s41598-021-84724-6openalex-extracted

Prevalence of spine degeneration diagnosis by type, age, gender, and obesity using Medicare data

inferredcites →https://doi.org/10.1109/3dv.2016.79openalex-extracted

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

inferredcites →https://doi.org/10.1109/cibec.2012.6473317openalex-extracted

2D PCA-based shape prior for level sets segmentation framework of the vertebral body

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr46437.2021.01350openalex-extracted

Coordinate Attention for Efficient Mobile Network Design

inferredcites →https://doi.org/10.1109/isbi.2006.1624935openalex-extracted

Automated Spinal Column Extraction and Partitioning

inferredcites →https://doi.org/10.1109/isbi.2018.8363715openalex-extracted

Fully automatic segmentation of lumbar vertebrae from CT images using cascaded 3D fully convolutional networks

inferredcites →https://doi.org/10.1109/lgrs.2018.2802944openalex-extracted

Road Extraction by Deep Residual U-Net

inferredcites →https://doi.org/10.1109/tbme.2009.2019765openalex-extracted

Atlas-Based Segmentation of Degenerated Lumbar Intervertebral Discs From MR Images of the Spine

inferredcites →https://doi.org/10.1109/tip.2021.3136619openalex-extracted

ECSU-Net: An Embedded Clustering Sliced U-Net Coupled With Fusing Strategy for Efficient Intervertebral Disc Segmentation and Classification

inferredcites →https://doi.org/10.1109/tmi.2013.2244903openalex-extracted

Spine Segmentation in Medical Images Using Manifold Embeddings and Higher-Order MRFs

inferredcites →https://doi.org/10.1186/1475-925x-12-48openalex-extracted

An improved level set method for vertebra CT image segmentation

inferredcites →https://doi.org/10.14316/pmp.2021.32.1.1openalex-extracted

Basic Physical Principles and Clinical Applications of Computed Tomography

inferredcites →https://doi.org/10.48550/arxiv.1709.01507openalex-extracted

Squeeze-and-Excitation Networks

inferredcites →https://doi.org/10.48550/arxiv.1712.01509openalex-extracted

inferredcites →https://doi.org/10.48550/arxiv.1804.03999openalex-extracted

Attention U-Net: Learning Where to Look for the Pancreas

inferredcites →https://doi.org/10.48550/arxiv.2103.02907openalex-extracted

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