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2020 | OriginalPaper | Chapter

Multi-modal Feature Attention for Cervical Lymph Node Segmentation in Ultrasound and Doppler Images

Authors : Xiangling Fu, Tong Gao, Yuan Liu, Mengke Zhang, Chenyi Guo, Ji Wu, Zhili Wang

Published in: Neural Information Processing

Publisher: Springer International Publishing

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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.

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next chapter MultiTune: Adaptive Integration of Multiple Fine-Tuning Models for Image Classification

Souce code for Auto Cropping SSD: https://github.com/RAY9874/Extract-us-region.

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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Title: Multi-modal Feature Attention for Cervical Lymph Node Segmentation in Ultrasound and Doppler Images
Authors: Xiangling Fu
Tong Gao
Yuan Liu
Mengke Zhang
Chenyi Guo
Ji Wu
Zhili Wang
Publisher: Springer International Publishing
Book: Neural Information Processing
Print ISBN: 978-3-030-63819-1

Electronic ISBN: 978-3-030-63820-7

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-63820-7_55

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