Automated tumor localization and segmentation through hybrid neural network

Ahmad Qasem; Hui Liu; Zhiguo Zhou

doi:10.1117/12.2613241

4 April 2022 Automated tumor localization and segmentation through hybrid neural network

Ahmad Qasem, Hui Liu, Zhiguo Zhou

Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 1203420 (2022) https://doi.org/10.1117/12.2613241
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Tumor localization is a critical step in head & neck cancer diagnosis and treatment. It allows for treatment planning and accurate projection of tumor spread. In this study, our goal is to develop a fully automated hybrid neural network (HNN) for localization and segmentation of PET/CT images through a combination of Faster-RCNN and U-Net. HNN consists of two deep neural networks, they are (1) Faster-RCNN which is a leading model in object detection is used to localize tumors in PET images, and (2) U-Net which has achieved great success in medical image segmentation is used for tumor segmentation in the HNN. The testing stage consists of two steps (1) tumor localization and segmentation, where each PET slice was fed into the trained Faster-RCNN to produce a class label and bounding box coordinates, and then the CT slice was cropped according to the predicted bounding box and passed to the U-Net for segmentation; (2) the predicted mask is post-processed by applying OTSU thresholding method. The experimental results demonstrated that HNN can obtain promising segmentation performance.

Citation Download Citation

Ahmad Qasem, Hui Liu, and Zhiguo Zhou "Automated tumor localization and segmentation through hybrid neural network", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 1203420 (4 April 2022); https://doi.org/10.1117/12.2613241

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