Deep Learning Approaches for Vertebrae Segmentation in CT Images

Introduction to Vertebrae Segmentation in CT Images

Vertebrae segmentation in computed tomography (CT) images is crucial for diagnosing and treating various spinal conditions. However, the task is challenging due to the complex anatomy of the spine, variations among patients, and the presence of overlapping structures. Recent advancements in deep learning have shown promise in automating this process with high accuracy and efficiency.

Patch-Based Deep Learning Models for Vertebrae Segmentation

Patch-Based Deep Belief Networks (PaDBNs)

One approach to vertebrae segmentation involves using patch-based deep belief networks (PaDBNs). This method automatically selects features from image patches and uses a contrastive divergence algorithm for unsupervised feature reduction. The weights are then fine-tuned in a supervised manner, and the discriminative features are used for classification. This model has demonstrated excellent performance in terms of accuracy and computational efficiency compared to state-of-the-art methods.

Stacked Sparse Autoencoder (SSAE)

Another patch-based method employs a stacked sparse autoencoder (SSAE) to extract discriminative features from unlabeled data. This approach divides 2D CT slices into overlapping patches and uses a random under-sampling module to balance the training data. The SSAE learns high-level features from pixel intensities, which are then used to classify whether each patch contains a vertebra. This method has shown high precision, recall, and accuracy across multiple datasets.

Advanced Deep Learning Architectures

CHASPPRAU-Net and MRU-Net

A novel deep learning model combines a cascaded hierarchical atrous spatial pyramid pooling residual attention U-Net (CHASPPRAU-Net) for spine segmentation and a 3D mobile residual U-Net (MRU-Net) for vertebrae recognition. This model uses spatial pyramid pooling layers and residual blocks for feature extraction, along with attention modules to focus on regions of interest. The MRU-Net processes axial, sagittal, and coronal views to form a 3D feature map for vertebrae recognition. This approach has achieved superior results compared to existing methods.

Spine-Transformers

Spine-Transformers utilize a two-stage deep learning solution for vertebra labeling and segmentation in arbitrary field-of-view CT images. The first stage involves a transformers-based 3D object detector that treats vertebra detection as a one-to-one set prediction problem. The second stage uses a multi-task encoder-decoder network for segmentation and refinement. This method has shown efficacy in handling volume orientation variations and achieving accurate segmentation.

Challenges and Solutions in Vertebrae Segmentation

Anatomical Variations and Multi-Vendor Data

The VerSe 2020 dataset addresses the challenge of anatomical variations by including cases with enumeration abnormalities and transitional vertebrae. This dataset, collected from multiple centers and scanner manufacturers, provides a robust benchmark for developing and testing segmentation algorithms.

Noise Reduction and Edge Sharpening

A deep learning algorithm has been developed to simultaneously reduce noise and sharpen edges in low-dose CT images. This method significantly improves image quality and the visibility of anatomical structures, making it suitable for clinical applications.

Conclusion

Deep learning has revolutionized vertebrae segmentation in CT images, offering high accuracy and efficiency. Patch-based models like PaDBNs and SSAE, advanced architectures like CHASPPRAU-Net and MRU-Net, and innovative solutions like Spine-Transformers and noise reduction algorithms are paving the way for more reliable and automated spinal analysis. These advancements hold great potential for improving clinical outcomes in the diagnosis and treatment of spinal conditions.

Vertebrae CT images deep learning

Summary

Deep Learning Approaches for Vertebrae Segmentation in CT Images

Introduction to Vertebrae Segmentation in CT Images

Patch-Based Deep Learning Models for Vertebrae Segmentation

Patch-Based Deep Belief Networks (PaDBNs)

Stacked Sparse Autoencoder (SSAE)

Advanced Deep Learning Architectures

CHASPPRAU-Net and MRU-Net

Spine-Transformers

Challenges and Solutions in Vertebrae Segmentation

Anatomical Variations and Multi-Vendor Data

Noise Reduction and Edge Sharpening

Conclusion

Sources and full results

Automatic Deep Feature Learning via Patch-Based Deep Belief Network for Vertebrae Segmentation in CT Images

The proposed patch-based deep belief networks (PaDBNs) model significantly reduces computational cost and improves accuracy in automatic CT vertebra segmentation compared to state-of-the-art methods.

CT-Based Automatic Spine Segmentation Using Patch-Based Deep Learning

Our patch-based deep learning approach using a stacked sparse autoencoder (SSAE) effectively segmented CT vertebrae, outperforming other models and providing a flexible, fast, and generalizable solution for clinical applications.

An Automated Deep Learning Approach for Spine Segmentation and Vertebrae Recognition Using Computed Tomography Images

The proposed deep learning model accurately segments and recognizes vertebrae in CT images, outperforming state-of-the-art methods.

A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data

The VerSe 2020 dataset, with anatomical variants and multi-vendor scanner data, enables the development and benchmarking of robust and accurate segmentation algorithms for spine CT images.

Deep Belief Network Based Vertebra Segmentation for CT Images

Our deep belief networks model provides more accurate vertebra segmentation in CT images compared to previous state-of-the-art methods.

Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters

The custom-designed CNN with tunable directional filters provides higher accuracy in fully automated determination of cervical vertebrae maturation stages compared to other pre-trained network models.

Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT

Deep learning algorithms can enable simultaneous noise reduction and improved image quality in low-dose lumbar spine CT, preserving edge sharpness.

Spine-transformers: Vertebra labeling and segmentation in arbitrary field-of-view spine CTs via 3D transformers

Spine-Transformers, a deep learning-based two-stage solution, effectively detects and segments 3D vertebrae in arbitrary field-of-view spine CT images.

Automatic Localization and Identification of Vertebrae in Spine CT via a Joint Learning Model with Deep Neural Networks

Our joint learning model with CNN J-CNN significantly improves vertebrae localization and identification in 3D spinal images, achieving a 10.12% improvement in identification rate and smaller localization errors compared to state-of-the-art methods.

Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network

Deep learning can predict bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal CT images, allowing for high-performance osteoporosis diagnosis.

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