Share Email Print
cover

Proceedings Paper • new

Automatic PET cervical tumor segmentation by deep learning with prior information
Author(s): Liyuan Chen; Chenyang Shen; Shulong Li; Genevieve Maquilan; Kevin Albuquerque; Michael R. Folkert; Jing Wang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057436 (2 March 2018); doi: 10.1117/12.2293926
Show Author Affiliations
Liyuan Chen, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Chenyang Shen, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Shulong Li, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Genevieve Maquilan, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Kevin Albuquerque, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Michael R. Folkert, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Jing Wang, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top