ISA-Net: Improved spatial attention network for PET-CT tumor segmentation

TL;DR

This paper introduces ISA-Net, a novel deep learning model that enhances tumor segmentation accuracy in PET-CT images by utilizing multi-scale convolutional features and dual-channel fusion, outperforming existing attention methods.

Contribution

The paper presents ISA-Net, an improved spatial attention network that effectively leverages multimodal PET-CT data for more accurate tumor segmentation, with demonstrated superior performance on clinical datasets.

Findings

Achieved DSC scores of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset.

Outperformed other attention-based segmentation methods.

Demonstrated good generalization across different tumor types.

Abstract

Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. Conclusions: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment.