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

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.

References in corpus (12)

• Automated Design of Deep Learning Methods for Biomedical Image Segmentation
• Attention U-Net: Learning Where to Look for the Pancreas
• The Medical Segmentation Decathlon
• CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
• MONAI: An open-source framework for deep learning in healthcare
• Why rankings of biomedical image analysis competitions should be interpreted with care
• DeepViT: Towards Deeper Vision Transformer
• The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes
• How Do Vision Transformers Work?
• Common Limitations of Image Processing Metrics: A Picture Story
• TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation
• STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Cited by in corpus (1)

• Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention