Abstract on deep learning for PET image analysis accepted @ SNMMI

Our abstract titled “A deep-learning-based fully automated segmentation approach to delineate tumors in FDG-PET images of patients with lung cancer” was accepted for an oral presentation at the 2018 SNMMI Annual Meeting. In this work, we developed and demonstrated the efficacy of a deep learning-based method for tumor segmentation in PET images. Since these images are very noisy and have limited spatial resolution, it was exciting to see the efficacy of the developed deep-learning method for segmenting these images! 

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