Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images

• URL: http://arxiv.org/abs/2503.17261v1
• Date: Fri, 21 Mar 2025 16:04:11 GMT
• Title: Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images
• Authors: Jie Mei, Chenyu Lin, Yu Qiu, Yaonan Wang, Hui Zhang, Ziyang Wang, Dong Dai,
• Abstract summary: Deep learning models are expected to address problems such as poor image quality, motion artifacts, and complex tumor morphology.<n>We introduce a large-scale PET-CT lung tumor segmentation dataset, termed PCLT20K, which comprises 21,930 pairs of PET-CT images from 605 patients.<n>We propose a cross-modal interactive perception network with Mamba (CIPA) for lung tumor segmentation in PET-CT images.
• Score: 29.523577037519985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lung cancer is a leading cause of cancer-related deaths globally. PET-CT is crucial for imaging lung tumors, providing essential metabolic and anatomical information, while it faces challenges such as poor image quality, motion artifacts, and complex tumor morphology. Deep learning-based models are expected to address these problems, however, existing small-scale and private datasets limit significant performance improvements for these methods. Hence, we introduce a large-scale PET-CT lung tumor segmentation dataset, termed PCLT20K, which comprises 21,930 pairs of PET-CT images from 605 patients. Furthermore, we propose a cross-modal interactive perception network with Mamba (CIPA) for lung tumor segmentation in PET-CT images. Specifically, we design a channel-wise rectification module (CRM) that implements a channel state space block across multi-modal features to learn correlated representations and helps filter out modality-specific noise. A dynamic cross-modality interaction module (DCIM) is designed to effectively integrate position and context information, which employs PET images to learn regional position information and serves as a bridge to assist in modeling the relationships between local features of CT images. Extensive experiments on a comprehensive benchmark demonstrate the effectiveness of our CIPA compared to the current state-of-the-art segmentation methods. We hope our research can provide more exploration opportunities for medical image segmentation. The dataset and code are available at https://github.com/mj129/CIPA.

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