当前位置： SCI文献检索 > COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE期刊下所有文献 > MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images.

MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images.

Abstract：

BACKGROUND AND OBJECTIVE:Automatic osteosarcoma tumor segmentation on computed tomography (CT) images is a challenging problem, as tumors have large spatial and structural variabilities. In this study, an automatic tumor segmentation method, which was based on a fully convolutional networks with multiple supervised side output layers (MSFCN), was presented. METHODS:Image normalization is applied as a pre-processing step for decreasing the differences among images. In the frame of the fully convolutional networks, supervised side output layers were added to three layers in order to guide the multi-scale feature learning as a contracting structure, which was then able to capture both the local and global image features. Multiple feature channels were used in the up-sampling portion to capture more context information, for the assurance of accurate segmentation of the tumor, with low contrast around the soft tissue. The results of all the side outputs were fused to determine the final boundaries of the tumors. RESULTS:A quantitative comparison of the 405 osteosarcoma manual segmentation results from the CT images showed that the average Dice similarity coefficient (DSC), average sensitivity, average Hammoude distance (HM) and F1-measure were 87.80%, 86.88%, 19.81% and 0.908, respectively. It was determined that, when compared with the other learning-based algorithms (for example, the fully convolution networks (FCN), U-Net method, and holistically-nested edge detection (HED) method), the MSFCN had the best performances in terms of DSC, sensitivity, HM and F1-measure. CONCLUSION:The results indicated that the proposed algorithm contributed to the fast and accurate delineation of tumor boundaries, which could potentially assist doctors in making more precise treatment plans.

journal_name

Comput Methods Programs Biomed

journal_title

Computer methods and programs in biomedicine

authors

Huang L,Xia W,Zhang B,Qiu B,Gao X

doi

10.1016/j.cmpb.2017.02.013

subject

Has Abstract

pub_date

2017-05-01 00:00:00

pages

67-74

eissn

0169-2607

issn

1872-7565

pii

S0169-2607(16)31092-6

journal_volume

143

pub_type

杂志文章

在线工具

MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images.