UNetVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTM (2501.07017v2)

Abstract: 3D medical image segmentation has progressed considerably due to Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), yet these methods struggle to balance long-range dependency acquisition with computational efficiency. To address this challenge, we propose UNETVL (U-Net Vision-LSTM), a novel architecture that leverages recent advancements in temporal information processing. UNETVL incorporates Vision-LSTM (ViL) for improved scalability and memory functions, alongside an efficient Chebyshev Kolmogorov-Arnold Networks (KAN) to handle complex and long-range dependency patterns more effectively. We validated our method on the ACDC and AMOS2022 (post challenge Task 2) benchmark datasets, showing a significant improvement in mean Dice score compared to recent state-of-the-art approaches, especially over its predecessor, UNETR, with increases of 7.3% on ACDC and 15.6% on AMOS, respectively. Extensive ablation studies were conducted to demonstrate the impact of each component in UNETVL, providing a comprehensive understanding of its architecture. Our code is available at https://github.com/tgrex6/UNETVL, facilitating further research and applications in this domain.

• The paper introduces UNetVL, a model that integrates Vision-LSTM and Chebyshev KAN to significantly enhance 3D medical image segmentation.
• It employs bidirectional mLSTM blocks and Chebyshev polynomial-based projections to capture detailed anatomical features and long-range dependencies.
• Evaluation on ACDC and AMOS2022 benchmarks reveals Dice score improvements of 7.3% and 15.6%, demonstrating its superior performance.

Enhancing 3D Medical Image Segmentation with UNetVL: An Innovative Approach

The integration of advanced deep learning methodologies in 3D medical image segmentation has often encountered the challenge of balancing computational efficiency with the need for capturing long-range dependencies. This paper introduces UNetVL, a sophisticated architecture designed to address these challenges by amalgamating Vision-LSTM (ViL) and Chebyshev Kolmogorov-Arnold Networks (KAN) into a cohesive model. UNetVL aims to refine the accuracy and robustness of 3D medical image segmentations, which are pivotal for precise diagnostic and treatment planning in clinical applications.

Architectural Insights and Innovations

At the core of the UNetVL architecture is the replacement of Vision Transformer (ViT) layers with Vision-LSTM (ViL), which is an enhancement of the xLSTM architecture. ViL exhibits strengths in memory and scalability by utilizing stacked mLSTM blocks characterized by bidirectional processing. This feature enables the efficient capture of spatial and contextual information, critical for addressing the complexity of 3D medical images.

Complementing the ViL is the Chebyshev KAN, which substitutes the conventional MLP-based univariate function with a sophisticated framework rooted in Chebyshev polynomials. These polynomials, known for their orthogonality over certain intervals, enhance UNetVL’s ability to approximate complex encoding distributions. The reconfiguration of up and down projection layers using KAN allows for the capture of intricate anatomical details, promoting accurate and refined segmentation outputs.

Performance and Experimental Evaluation

UNetVL was evaluated on the ACDC and AMOS2022 benchmark datasets, both of which represent significant challenges in multi-organ and multi-modal medical image segmentation. The outcomes demonstrated notable performance advancements, with increases in Dice scores of 7.3% on ACDC and 15.6% on AMOS when compared to its UNETR predecessor. These improvements underscore UNetVL's superior ability to handle multifaceted image segmentation tasks.

The methodology section details the sophisticated processing pipeline UNetVL employs—dividing inputs into non-overlapping patches, linear projection into patch tokens, and sequential processing through ViL block pairs. The incorporation of a CNN-based decoder further synthesizes multi-scale features into the final segmentation results.

Implications and Speculative Future Directions

The proposed UNetVL architecture offers profound implications for the future of AI-enhanced medical imaging. It not only demonstrates how innovative integrations of temporally adept models like ViL can enhance 3D medical image segmentation but also illustrates how the adaptability of novel polynomial-based networks like Chebyshev KAN can be leveraged to capture complex image features.

The potential for future developments lies in further optimizing the computational complexity of such models, which remains a concern when scaling to larger datasets. Investigating ways to streamline the KAN layer, while maintaining or even enhancing its performance capabilities, could be a promising direction. Additionally, exploring the flexibility of this architecture to accommodate other types of medical imaging data could widen its applicability in diverse clinical contexts.

Conclusion

The enhancement in segmentation accuracy offered by UNetVL positions it as a promising model in the domain of medical image processing. By effectively addressing computational challenges and improving dependency capture through innovative architectural changes, UNetVL could significantly impact clinical practices, providing precise segmentations necessary for enhanced patient outcomes. As the field progresses, such interdisciplinary approaches integrating machine learning innovations will likely continue to shape the future landscape of medical diagnostics.