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Node-Level Lymph Node Automatic Segmentation in CT Images Using Deep Parallel Structure-Related 3D U-Net Variant

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PRICAI 2024: Trends in Artificial Intelligence (PRICAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15284))

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Abstract

The delineation of Lymph Nodes (LNs) is pivotal in pinpointing therapeutic targets for radiotherapy in head and neck malignancies. Nevertheless, this endeavor poses a formidable challenge, primarily stemming from the suboptimal contrast against adjacent tissues. This investigation introduces a deep learning methodology aimed at automating the segmentation of LNs within CT scans, offering the following contributions: (1) Expanding upon the 3D Unet model, we incorporate a parallel block consisting of attention gate and squeeze & excitation modules. We extensively evaluate various versions of this parallel block and achieve favorable performance. (2) To address the slow decrease in Dice loss, we introduce a lightweight boundary refinement module. Our proposed method is assessed on a dataset comprising 103 patients and 603 Lymph Nodes (LNs), with 452 nodes used for training and 151 nodes for testing. The node-level Dice similarity coefficient achieved by our method reaches an impressive 0.833.

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Acknowledgment

This work was supported by the China Postdoctoral Science Foundation (No. 2023M742568), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 24KJB510042) and Kunshan Government Research Fund (No. 24KKSGR028).

Author information

Authors and Affiliations

  1. School of Future Science and Engineering, Soochow University, Suzhou, China

    Shi Cheng & Tao Peng

  2. Center of Stomatology, The Second Affiliated Hospital of Soochow University, Suzhou, China

    Quan Li

  3. Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Guangyu Zhang

  4. Graduate Program of Medical Physics and Data Science Research Center, Duke Kunshan University, Kunshan, Jiangsu, China

    Lei Zhang

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  1. Shi Cheng
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  2. Quan Li
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  3. Guangyu Zhang
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  4. Lei Zhang
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  5. Tao Peng
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Corresponding author

Correspondence to Tao Peng .

Editor information

Editors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Rafik Hadfi

  2. Lincoln University, Christchurch, New Zealand

    Patricia Anthony

  3. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Alok Sharma

  4. Kyoto University, Kyoto, Japan

    Takayuki Ito

  5. University of Tasmania, Tasmania, TAS, Australia

    Quan Bai

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Cheng, S., Li, Q., Zhang, G., Zhang, L., Peng, T. (2025). Node-Level Lymph Node Automatic Segmentation in CT Images Using Deep Parallel Structure-Related 3D U-Net Variant. In: Hadfi, R., Anthony, P., Sharma, A., Ito, T., Bai, Q. (eds) PRICAI 2024: Trends in Artificial Intelligence. PRICAI 2024. Lecture Notes in Computer Science(), vol 15284. Springer, Singapore. https://doi.org/10.1007/978-981-96-0125-7_9

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  • DOI: https://doi.org/10.1007/978-981-96-0125-7_9

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