Deep-learning-based extraprostatic nodal lesion segmentation on 18F-fluciclovine PET

Tonghe Wang; Lei Yang; Eduard Schreibmann; Justin Roper; David M. Schuster; Jeffrey D. Bradley; Tian Liu; Ashesh B. Jani; Xiaofeng Yang

doi:10.1117/12.2611759

4 April 2022 Deep-learning-based extraprostatic nodal lesion segmentation on 18F-fluciclovine PET

Tonghe Wang, Lei Yang, Eduard Schreibmann, Justin Roper, David M. Schuster, Jeffrey D. Bradley, Tian Liu, Ashesh B. Jani, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 120360P (2022) https://doi.org/10.1117/12.2611759
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

We propose a learning-based method to automatically segment extraprostatic nodal lesion from 18F-fluciclovine (anti-1- amino-3-[18F] fluorocyclobutane-1-carboxylic acid) PET images. Our proposed method, named hierarchical activation network, consists of three main subnetworks: a fully convolutional one-stage object detection (FCOS) network and a mask module, and a hierarchical convolutional block. While FCOS is employed to detect the view-of-interests (VOIs) of extraprostatic nodal lesion. Hierarchical convolutional block is used to derive activation map to boost the classification accuracy around lesion boundary. This is followed by the binary segmentation of extraprostatic nodal lesion within the detected VOI by mask module. To evaluate the proposed method, we retrospectively investigated 92 lesions with 18F- fluciclovine PET acquired. On each dataset, the extraprostatic lesions were delineated by physicians and was served as ground truth and training target. The proposed method was trained and evaluated by a five-fold cross validation strategy. The average DSC among all lesions is close to 0.7. The proposed method has great potential in improving the efficiency and mitigating the observer-dependence in extraprostatic lesion contouring for radiation therapy.

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Citation Download Citation

Tonghe Wang, Lei Yang, Eduard Schreibmann, Justin Roper, David M. Schuster, Jeffrey D. Bradley, Tian Liu, Ashesh B. Jani, and Xiaofeng Yang "Deep-learning-based extraprostatic nodal lesion segmentation on 18F-fluciclovine PET", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 120360P (4 April 2022); https://doi.org/10.1117/12.2611759

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