RotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation

TL;DR

RotCAtt-TransUNet++ is introduced, a novel architecture designed for robust segmentation of intricate cardiac structures, which enhances global context mod-eling through multiscale feature aggregation and nested skip connections in the encoder.

Abstract

Cardiovascular disease remains a predominant global health concern, responsible for a significant portion of mortality worldwide. Accurate segmentation of cardiac medical imaging data is pivotal in mitigating fatality rates associated with cardiovascular conditions. However, existing state-of-the-art (SOTA) neural networks, including both CNN-based and Transformer-based approaches, exhibit limitations in practical applicability due to their inability to effectively capture inter-slice connections alongside intra-slice information. This deficiency is particularly evident in datasets featuring intricate, long-range details along the z-axis, such as coronary arteries in axial views. Additionally, SOTA methods fail to differentiate non-cardiac components from myocardium in segmentation, leading to the "spraying" phenomenon. To address these challenges, we present RotCAtt-TransUNet++, a novel architecture tailored for robust segmentation of complex cardiac structures. Our approach emphasizes modeling global contexts by aggregating multiscale features with nested skip connections in the encoder. It integrates transformer layers to capture interactions between patches and employs a rotatory attention mechanism to capture connectivity between multiple slices (inter-slice information). Additionally, a channel-wise cross-attention gate guides the fused multi-scale channel-wise information and features from decoder stages to bridge semantic gaps. Experimental results demonstrate that our proposed model outperforms existing SOTA approaches across four cardiac datasets and one abdominal dataset. Importantly, coronary arteries and myocardium are annotated with near-perfect accuracy during inference. An ablation study shows that the rotatory attention mechanism effectively transforms embedded vectorized patches in the semantic dimensional space, enhancing segmentation accuracy.

Figures (11)

• Figure 1: Visualization of multi-scale feature maps after dense downsampling. The multi-scale learning enables the model to capture high-level features while preserving spatial information. Patches are depicted solely on the first feature map of $X1, X2, X3$ following convolutional operations and dense skip connections.
• Figure 2: RotCAtt-TransUNet++ architecture: Combining rotatory attention mechanism with channel-wise attention gates for enhanced feature fusion in decoder. Leveraging the Transformer-UNet Hybrid Model with enriched nested skip connections for multiscale feature extraction.
• Figure 3: The rotatory attention first uses the target phrase to compute new representations for the left (previous slice) and right (next slice) context using attention mechanism to capture the most important inter-connectivity information to current slice from two adjacent slices. Then, the second step use these left and right representations to calculate the new representations for the target phrase to integrate the most important information into the actual current slice itself.
• Figure 4: The Channel-wise Attention Module integrates multi-scale context by incorporating cross attention from a channel-wise perspective. Its objective is to capture local cross-channel interactions, enabling an adaptive scheme for effectively merging multi-scale channel-wise features. This approach addresses potential scale semantic gaps through collaborative learning, rather than relying on independent connections, thereby resolving inconsistencies in semantic levels.
• Figure 5: The training graphs depict the performance of the RotCAtt-TransUNet++ model across five distinct datasets. Remarkably, our network excels when applied to cardiac data, benefiting from robust long-range interslice connectivity. However, we encountered challenges with the Synapse dataset, failing to meet anticipated performance levels. In case of ImageCAS, due to the dominance of background over coronary arteries in binary segmentation, our model exhibited limitations but still outperformed the baseline method (3D UNet) proposed by imagecas