Automatic PET cervical tumor segmentation by combining deep learning and anatomic prior

Physics in Medicine and Biology
Liyuan ChenJing Wang

Abstract

Cervical tumor segmentation on 3D 18FDG PET images is a challenging task because of the proximity between cervix and bladder, both of which can uptake 18FDG tracers. This problem makes traditional segmentation based on intensity variation methods ineffective and reduces overall accuracy. Based on anatomy knowledge, including 'roundness' of the cervical tumor and relative positioning between the bladder and cervix, we propose a supervised machine learning method that integrates convolutional neural network (CNN) with this prior information to segment cervical tumors. First, we constructed a spatial information embedded CNN model (S-CNN) that maps the PET image to its corresponding label map, in which bladder, other normal tissue, and cervical tumor pixels are labeled as  -1, 0, and 1, respectively. Then, we obtained the final segmentation from the output of the network by a prior information constrained (PIC) thresholding method. We evaluated the performance of the PIC-S-CNN method on PET images from 50 cervical cancer patients. The PIC-S-CNN method achieved a mean Dice similarity coefficient (DSC) of 0.84 while region-growing, Chan-Vese, graph-cut, fully convolutional neural networks (FCN) based FCN-8 stride, and FCN-2 stride, ...Continue Reading

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Citations

Mar 12, 2021·European Journal of Nuclear Medicine and Molecular Imaging·Kyeong Taek OhSun Kook Yoo
Mar 28, 2021·European Journal of Nuclear Medicine and Molecular Imaging·Marta FerreiraRoland Hustinx
Apr 3, 2021·Annual Review of Biomedical Engineering·Habib Zaidi, Issam El Naqa

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