Abstract
Cervical lymph node disease is a kind of cervical disease with a high incidence. Accurate detection of lymph nodes can greatly improve the performance of the computer-aided diagnosis systems. Presently, most studies have focused on classifying lymph nodes in a given ultrasound image. However, ultrasound has a poor discrimination of different tissues such as blood vessel and lymph node. When solving confused tasks like detecting cervical lymph nodes, ultrasound imaging becomes inappropriate. In this study, we combined two common modalities to detect cervical lymph nodes: ultrasound and Doppler. Then a multimodal fusion method is proposed, which made full use of the complementary information between the two modalities to distinguish the lymph and other tissues. 1054 pairs of ultrasound and Doppler images are used in the experiment. As a result, the proposed multimodal fusion method is 3% higher (DICE value) than the baseline methods in segmentation results.
This work is supported by Beijing Municipal Natural Science Foundation (No. L192026).
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Notes
- 1.
Souce code for Auto Cropping SSD: https://github.com/RAY9874/Extract-us-region.
- 2.
Source code for the proposed method U-net-dual-steam + FSM: https://github.com/RAY9874/Multimodal-Feature-Attention-for-Cervical-Lymph-Node-Segmentation-in-Ultrasound-and-Doppler-Images.
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Fu, X. et al. (2020). Multi-modal Feature Attention for Cervical Lymph Node Segmentation in Ultrasound and Doppler Images. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_55
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