CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation (2103.03024v1)

Abstract: Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. The convolutional operations used in these networks, however, inevitably have limitations in modeling the long-range dependency due to their inductive bias of locality and weight sharing. Although Transformer was born to address this issue, it suffers from extreme computational and spatial complexities in processing high-resolution 3D feature maps. In this paper, we propose a novel framework that efficiently bridges a {\bf Co}nvolutional neural network and a {\bf Tr}ansformer {\bf (CoTr)} for accurate 3D medical image segmentation. Under this framework, the CNN is constructed to extract feature representations and an efficient deformable Transformer (DeTrans) is built to model the long-range dependency on the extracted feature maps. Different from the vanilla Transformer which treats all image positions equally, our DeTrans pays attention only to a small set of key positions by introducing the deformable self-attention mechanism. Thus, the computational and spatial complexities of DeTrans have been greatly reduced, making it possible to process the multi-scale and high-resolution feature maps, which are usually of paramount importance for image segmentation. We conduct an extensive evaluation on the Multi-Atlas Labeling Beyond the Cranial Vault (BCV) dataset that covers 11 major human organs. The results indicate that our CoTr leads to a substantial performance improvement over other CNN-based, transformer-based, and hybrid methods on the 3D multi-organ segmentation task. Code is available at \def\UrlFont{\rm\small\ttfamily} \url{https://github.com/YtongXie/CoTr}

• The paper demonstrates that integrating CNNs with a deformable Transformer module significantly improves 3D segmentation accuracy on medical images.
• The deformable self-attention mechanism selectively captures long-range dependencies, reducing computational and spatial complexity.
• Experiments on the BCV dataset show that CoTr outperforms both traditional and advanced models, setting a new benchmark in medical image segmentation.

Analyzing CoTr: Bridging CNN and Transformer for 3D Medical Image Segmentation

The paper introduces CoTr, a hybrid model designed to enhance 3D medical image segmentation by effectively integrating Convolutional Neural Networks (CNNs) with Transformer architectures. This approach addresses the limitations inherent in these two models, particularly CNN's localized modeling focus and Transformer's computational inefficiencies in processing high-resolution data. The resulting model aims to balance precision and efficiency in segmenting complex 3D medical images.

The methodological innovation primarily lies in the deformable Transformer (DeTrans) module. DeTrans employs a deformable self-attention mechanism, which models long-range dependencies selectively by focusing on key points rather than processing the entire image uniformly. This selective attention drastically reduces computational and spatial complexity, thus enabling efficient handling of multi-scale and high-resolution feature maps.

The paper evaluates CoTr extensively on the BCV dataset, which involves a multi-organ segmentation task including organs like the spleen, liver, and pancreas. The experimental results reveal a significant performance boost of CoTr over traditional CNN methods, standalone Transformer architectures, and other hybrid counterparts. CoTr achieves an average Dice score improvement, illustrating its superior capability in accurately segmenting anatomical structures.

Key findings include:

• CoTr consistently outperforms not only CNN-based methods like ASPP and Non-local, but also advanced Transformer-based approaches such as SETR with pre-trained Vision Transformer models.
• The deformable self-attention module enables CoTr to leverage high-resolution and multi-scale feature maps effectively, unlike existing Transformer models which struggle with the associated computational cost.
• The hybrid model architecture demonstrates advantages in initialization and training convergence, particularly in medical imaging contexts where datasets are limited compared to traditional vision tasks.

The implications of this research are multifaceted. From a practical standpoint, CoTr sets a new benchmark for medical image segmentation, particularly in processing 3D data with improved robustness and accuracy. Theoretically, it highlights the potential of deformable attention mechanisms to mitigate computational bottlenecks typically faced by Transformers, suggesting avenues for future exploration in other domains requiring high-dimensional data processing.

Furthermore, CoTr opens up several possible directions for future research. Exploring its application across varying medical imaging tasks, beyond organ segmentation, could reveal insights into its adaptability and generalization capabilities. Additionally, extending the deformable self-attention mechanism to other forms of Transformers could further enhance their utility across various fields of AI research.

In conclusion, the paper presents a compelling case for the hybrid integration of CNN and Transformer models, proposing a novel approach to overcoming specific challenges inherent in each, particularly within the domain of medical image segmentation. CoTr's performance, underscored by its architectural innovations, signifies a valuable contribution to advancing AI methods in healthcare applications.