Automatic Whole Spine Segmentation of T2-weighted MRI using a Two-Phase Deep Learning Approach

Centrala begrepp

A deep learning approach called SPINEPS that can automatically segment 14 spinal structures, including vertebrae substructures, intervertebral discs, spinal cord, and spinal canal, in T2-weighted MRI scans of the whole spine.

Sammanfattning

The authors present SPINEPS, a two-phase deep learning approach for semantic and instance segmentation of 14 spinal structures in T2-weighted whole-body MRI scans.
In the first phase, a semantic segmentation model is used to segment the scans into 14 different spinal structures, including 10 vertebral substructures, intervertebral discs, spinal cord, and spinal canal. In the second phase, a sliding window instance segmentation model is applied to the semantic segmentation to identify individual vertebrae instances.
The approach was trained and evaluated on data from the public SPIDER dataset, a subset of the German National Cohort (NAKO), and an in-house dataset. On the SPIDER test set, SPINEPS outperformed a baseline nnUNet model across various metrics, including Dice similarity coefficient, average symmetric surface distance, and instance-wise segmentation quality.
When trained only on automated annotations derived from a combination of existing segmentation models and MR-to-CT translation, the approach achieved Dice scores of 0.90 for vertebrae, 0.96 for intervertebral discs, and 0.95 for the spinal canal on a manually corrected NAKO test set. Further incorporating the manually annotated SPIDER dataset improved these scores to 0.92, 0.97, and 0.96, respectively.
Qualitative evaluation on the in-house dataset demonstrated the robustness of the approach to out-of-distribution samples from different scanners, field strengths, and spatial resolutions, as well as cases with pathologies.

Statistik

The vertebra corpus Dice similarity coefficient was 0.96.
The intervertebral disc Dice similarity coefficient was 0.97.
The spinal canal Dice similarity coefficient was 0.96.
The spinal cord Dice similarity coefficient was 0.97.

Citat

"The proposed segmentation approach offers robust segmentation of 14 spinal structures in T2w sagittal images, including the spinal cord, spinal canal, intervertebral discs, endplate, sacrum, and vertebrae."
"Training on auto-generated annotations and evaluating on manually corrected test data from the GNC yielded global dice scores of 0.900 for vertebrae, 0.960 for intervertebral discs, and 0.947 for the spinal canal."

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SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation

by Hend... på arxiv.org 04-23-2024

https://arxiv.org/pdf/2402.16368.pdf

Djupare frågor

How could the SPINEPS approach be extended to segment additional spinal structures beyond the 14 considered in this study

To extend the SPINEPS approach to segment additional spinal structures beyond the 14 considered in this study, several modifications and enhancements could be implemented.

Data Augmentation: Incorporating a more extensive dataset with annotations for a broader range of spinal structures would be crucial. This would involve manually annotating additional structures or utilizing transfer learning techniques from models trained on datasets with annotations for these structures.

Model Architecture: Adapting the neural network architecture to accommodate the segmentation of new structures would be necessary. This may involve adding new output branches to the existing model or modifying the existing architecture to handle a larger number of classes.

Training Strategy: Implementing a multi-task learning approach could be beneficial, where the model is trained to simultaneously segment multiple spinal structures. This would require careful consideration of loss functions and training strategies to ensure optimal performance.

Post-Processing Techniques: Developing specific post-processing techniques tailored to the segmentation of new structures could improve the accuracy and robustness of the model. This may involve refining the instance-wise segmentation process or incorporating additional refinement steps.

Validation and Evaluation: Extensive validation and evaluation on a diverse dataset containing the new structures would be essential to assess the model's performance accurately. Fine-tuning the model based on feedback from validation results would be crucial in optimizing segmentation accuracy.

By implementing these strategies, the SPINEPS approach could be extended to effectively segment additional spinal structures, providing a more comprehensive and detailed analysis of spinal anatomy in MRI images.

What are the potential limitations of using automated annotations derived from MR-to-CT translation, and how could these be addressed

Using automated annotations derived from MR-to-CT translation may have certain limitations that could impact the segmentation accuracy and generalizability of the model. Some potential limitations include:

Annotation Quality: The quality of annotations generated through translation may vary, leading to inaccuracies in the training data. Addressing this limitation would require thorough validation and refinement of the automated annotations to ensure their accuracy.

Limited Structure Coverage: The MR-to-CT translation approach may not capture all spinal structures present in MRI images, leading to incomplete annotations. To address this limitation, additional manual annotations or data augmentation techniques could be employed to enhance the dataset's comprehensiveness.

Domain Adaptation: MR and CT images have inherent differences in contrast and appearance, which could affect the quality of annotations derived from translation. Implementing domain adaptation techniques to bridge the gap between MR and CT images could help improve annotation accuracy.

Generalizability: The model trained on automated annotations may lack generalizability to unseen datasets or variations in imaging conditions. To address this limitation, transfer learning approaches or domain adaptation techniques could be utilized to enhance the model's ability to generalize across different datasets.

By addressing these limitations through rigorous validation, data augmentation, domain adaptation, and generalizability strategies, the impact of using automated annotations derived from MR-to-CT translation could be mitigated, improving the overall performance of the segmentation model.

How could the SPINEPS approach be adapted to work with other MRI modalities beyond T2-weighted scans, such as T1-weighted or diffusion-weighted imaging

Adapting the SPINEPS approach to work with other MRI modalities beyond T2-weighted scans, such as T1-weighted or diffusion-weighted imaging, would require specific modifications and considerations:

Data Preprocessing: Preprocess the input images to ensure compatibility with the model architecture and training process. This may involve adjusting the intensity ranges, resolution, and contrast of the images to align with the model requirements.

Model Architecture: Modify the neural network architecture to accommodate the different characteristics of T1-weighted or diffusion-weighted images. This may involve fine-tuning the model's parameters or adding additional layers to handle the unique features of these modalities.

Training Data: Curate a dataset containing T1-weighted or diffusion-weighted MRI scans with corresponding annotations for the spinal structures of interest. Ensure the dataset is diverse and representative of the variations present in different modalities.

Transfer Learning: Utilize transfer learning techniques to leverage pre-trained models on T2-weighted scans and adapt them to work with T1-weighted or diffusion-weighted images. This can help expedite the training process and improve model performance.

Validation and Testing: Thoroughly validate the adapted model on a separate dataset of T1-weighted or diffusion-weighted images to assess its performance and generalizability. Fine-tune the model based on validation results to optimize segmentation accuracy.

By implementing these adaptations and considerations, the SPINEPS approach can be effectively adapted to work with other MRI modalities, providing comprehensive segmentation of spinal structures across different imaging techniques.