Abstract
Clinicians usually examine and diagnose patients with multimodality images such as CT and MRI because different modality data of the same anatomical structure are often complementary. This can provide doctors with a variety of information and help doctors to make accurate diagnoses. Inspired by this, the paper proposes a novel method of collaborative spinal segmentation based on spinal CT and MRI images. We use Siam network as architecture and ResNet50 as backbone network to extract high-level semantic features and low-level detail features of two modal images at the same time. Firstly, the high-level feature is enhanced by expanding the receptive field, and then it is input into the channel and spatial attention structure to achieve the optimal combination of high-level semantic information with the help of average pooling and maximum pooling, and learn the mutual information between different modal images. The learned high-level semantic correlation of different modalities will be combined with the up-sampled low-level features for maintaining the uniqueness of their respective modality, and finally the spinal segmentation results of the two modal images will be obtained at the same time. The experimental results show that the performance of multi-modal co-segmentation is better than that of single-modal co-segmentation and ResNet50 segmentation. All codes and data described are available at: https://github.com/1shero/CoSeg_CSA_MLMI.
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Acknowledgement
This study is supported by the Medical-Industrial Integration Project of Fudan University.
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Zou, Y., Shi, Y. (2021). Co-segmentation of Multi-modality Spinal Image Using Channel and Spatial Attention. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_30
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DOI: https://doi.org/10.1007/978-3-030-87589-3_30
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