Architecture Analysis and Benchmarking of 3D U-shaped Deep Learning Models for Thoracic Anatomical Segmentation (2402.03230v2)

Abstract: Recent rising interests in patient-specific thoracic surgical planning and simulation require efficient and robust creation of digital anatomical models from automatic medical image segmentation algorithms. Deep learning (DL) is now state-of-the-art in various radiological tasks, and U-shaped DL models have particularly excelled in medical image segmentation since the inception of the 2D UNet. To date, many variants of U-shaped models have been proposed by the integration of different attention mechanisms and network configurations. Systematic benchmark studies which analyze the architecture of these models by leveraging the recent development of the multi-label databases, can provide valuable insights for clinical deployment and future model designs, but such studies are still rare. We conduct the first systematic benchmark study for variants of 3D U-shaped models (3DUNet, STUNet, AttentionUNet, SwinUNETR, FocalSegNet, and a novel 3D SwinUnet with four variants) with a focus on CT-based anatomical segmentation for thoracic surgery. Our study systematically examines the impact of different attention mechanisms, the number of resolution stages, and network configurations on segmentation accuracy and computational complexity. To allow cross-reference with other recent benchmarking studies, we also included a performance assessment of the BTCV abdominal structural segmentation. With the STUNet ranking at the top, our study demonstrated the value of CNN-based U-shaped models for the investigated tasks and the benefit of residual blocks in network configuration designs to boost segmentation performance.

• The paper establishes that STUNet outperforms other models in both segmentation accuracy and computational efficiency for thoracic CT imaging.
• The paper finds that attention mechanisms offer limited improvement in segmentation, underscoring the need for task-specific model design.
• The paper highlights that optimized network configurations, including enhanced skip connections and residual blocks, significantly boost segmentation performance.

Benchmarking 3D U-Shaped Models for CT-Based Anatomical Segmentation in Thoracic Surgical Planning

Introduction

The quest for precision in thoracic surgical planning has led to an increased reliance on 3D anatomical segmentation from pre-operative medical images. Deep Learning (DL), especially with Convolutional Neural Networks (CNNs), has become a prominent tool in enhancing the efficiency and accuracy of these segmentations. U-shaped models, specifically the various 3D adaptations of the UNet architecture, stand out for their robust performance in medical image segmentation tasks. This paper presents a benchmark analysis of several U-shaped models, comparing their segmentation performance and computational efficiency in the context of thoracic surgery planning.

Benchmark Study

The research embarked on the first benchmark paper focusing on variants of 3D U-shaped models, including 3DUNet, STUNet, AttentionUNet, SwinUNETR, FocalSegNet, and a novel adaptation known as 3D SwinUnet with four variants. These models were evaluated based on their ability to segment anatomical structures pertinent to thoracic surgery from CT scans, using the TotalSegmentator dataset for comprehensive validation. The paper aimed at assessing the models' accuracy, computational complexity, and how different architectural elements—such as attention mechanisms, resolution stages, and network configurations—affect segmentation performance.

Key Findings

The benchmark results reveal several critical insights:

• STUNet's Superior Performance: Among the evaluated models, STUNet showcased the best overall performance considering both accuracy and computational efficiency. It consistently ranked highest across various metrics, emphasizing the value of CNN-based U-shaped models in the studied applications.
• Impact of Attention Mechanisms: Despite the theoretical benefits of attention mechanisms in improving model performance, the paper found no significant advantage in segmentation outcomes across models with varying attention mechanisms. This suggests that the effectiveness of such mechanisms may vary depending on the specific task and data characteristics.
• Importance of Network Configuration: Altering the number of resolution stages and designing network configurations, such as skip connections and upsampling and downsampling operations, substantially influence the models' performance. Specifically, incorporating residual blocks and optimizing upsampling techniques emerged as effective strategies for enhancing segmentation results.
• Challenges with Pure Transformer Models: The 3DSwinUnet, a pure Transformer-based model, underperformed compared to its CNN counterparts. However, modifying its architecture with elements like residual blocks and alternative upsampling methods led to considerable improvements, underscoring the potential benefits of hybrid architectures.

Implications and Future Directions

The findings of this paper have both practical and theoretical implications for the development and deployment of deep learning models in medical imaging. For practitioners, the benchmark provides a robust reference for selecting suitable models for thoracic surgical planning applications, prioritizing STUNet for its balance between accuracy and efficiency. From a research perspective, the paper highlights the nuanced impacts of architectural choices on model performance, encouraging further exploration into hybrid models that combine the strengths of CNNs and Transformers. Future work may delve into expanding the benchmark across different datasets, tasks, and imaging modalities, alongside investigating novel architectures and training strategies to push the frontiers of medical image segmentation.

Conclusion

This benchmark paper offers valuable insights into the performance of various 3D U-shaped models in the context of thoracic surgical planning, confirming the effectiveness of CNN-based approaches while inviting further exploration into architectural optimizations and hybrid models. The comprehensive evaluation underscores the need for careful architectural decisions to maximize segmentation accuracy and computational efficiency, paving the way for enhanced patient-specific surgical planning and simulation.