Automatic PET cervical tumor segmentation by deep learning with prior information

Liyuan Chen, Chenyang Shen, Shulong Li, Genevieve Maquilan, Kevin Albuquerque, Michael R. Folkert, Jing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


Cervical tumor segmentation on 3D 18FDG PET images is a challenging task due to the proximity between cervix and bladder. Since bladder has high capacity of 18FDG tracers, bladder intensity is similar to cervical tumor intensity in the PET image. This inhibits traditional segmentation methods based on intensity variation of the image to achieve high accuracy. We propose a supervised machine learning method that integrates a convolutional neural network (CNN) with prior information of cervical tumor. In the proposed prior information constraint CNN (PIC-CNN) algorithm, we first construct a CNN to weaken the bladder intensity value in the image. Based on the roundness of cervical tumor and relative positioning information between bladder and cervix, we obtain the final segmentation result from the output of the network by an auto-thresholding method. We evaluate the performance of the proposed PIC-CNN method on PET images from 50 cervical cancer patients whose cervix and bladder are abutting. The PIC-CNN method achieves a mean DSC value of 0.84 while transfer learning method based on fully convolutional neural networks (FCN) achieves 0.77 DSC. In addition, traditional segmentation methods such as automatic threshold and region-growing method only achieve 0.59 and 0.52 DSC values, respectively. The proposed method provides a more accurate way for segmenting cervical tumor in 3D PET image.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage Processing
ISBN (Electronic)9781510616370
StatePublished - Jan 1 2018
EventMedical Imaging 2018: Image Processing - Houston, United States
Duration: Feb 11 2018Feb 13 2018


OtherMedical Imaging 2018: Image Processing
Country/TerritoryUnited States


  • cervical tumor segmentation
  • CNN
  • PET image

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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