2 March 2018 Automatic PET cervical tumor segmentation by deep learning with prior information
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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.
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Liyuan Chen, Chenyang Shen, Shulong Li, Genevieve Maquilan, Kevin Albuquerque, Michael R. Folkert, Jing Wang, "Automatic PET cervical tumor segmentation by deep learning with prior information ", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057436 (2 March 2018); doi: 10.1117/12.2293926; https://doi.org/10.1117/12.2293926
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