10 papers analyzed
These studies suggest that various deep learning models, including SSAE, PaDBNs, Spine-Transformers, OP-convNet, and other novel algorithms, effectively segment vertebrae in CT images, often outperforming state-of-the-art methods in accuracy, precision, and computational efficiency.
Vertebrae segmentation in CT images is a critical task in medical imaging, aiding in various clinical applications such as surgical planning, diagnosis, and assessment of spinal conditions. Deep learning techniques have emerged as powerful tools for automating this process, addressing challenges like anatomical variability, complex spine structures, and image quality issues.
Patch-Based Deep Learning Approaches:
Transformer-Based Models:
Multi-Modality Segmentation:
Hierarchical and Hybrid Methods:
Advanced Network Architectures:
Large-Scale Datasets and Benchmarking:
Deep learning techniques have significantly advanced the field of vertebrae segmentation in CT images. Patch-based methods, transformer models, multi-modality systems, and advanced network architectures have all contributed to improved accuracy and efficiency. The use of large-scale datasets for training and validation has further enhanced the robustness and generalizability of these models, making them highly suitable for clinical applications.
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