A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation

• URL: http://arxiv.org/abs/2502.05396v1
• Date: Sat, 08 Feb 2025 00:52:45 GMT
• Title: A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation
• Authors: Canxuan Gang,
• Abstract summary: Convolutional neural networks (CNNs) have dominated the field, achieving significant success in 3D medical image segmentation. Recent transformer-based models, such as TransUNet and nnFormer, have demonstrated promise in addressing these limitations. This paper introduces a novel, fully convolutional-free model based on transformer architecture and self-attention mechanisms.
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• Abstract: Segmentation of 3D medical images is a critical task for accurate diagnosis and treatment planning. Convolutional neural networks (CNNs) have dominated the field, achieving significant success in 3D medical image segmentation. However, CNNs struggle with capturing long-range dependencies and global context, limiting their performance, particularly for fine and complex structures. Recent transformer-based models, such as TransUNet and nnFormer, have demonstrated promise in addressing these limitations, though they still rely on hybrid CNN-transformer architectures. This paper introduces a novel, fully convolutional-free model based on transformer architecture and self-attention mechanisms for 3D medical image segmentation. Our approach focuses on improving multi-semantic segmentation accuracy and addressing domain adaptation challenges between thick and thin slice CT images. We propose a joint loss function that facilitates effective segmentation of thin slices based on thick slice annotations, overcoming limitations in dataset availability. Furthermore, we present a benchmark dataset for multi-semantic segmentation on thin slices, addressing a gap in current medical imaging research. Our experiments demonstrate the superiority of the proposed model over traditional and hybrid architectures, offering new insights into the future of convolution-free medical image segmentation.

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