MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images. (May 2017)

• Record Type: Journal Article
• Authors: Huang, Lin; Xia, Wei; Zhang, Bo; Qiu, Bensheng; Gao, Xin
• Is Part Of: Computer methods and programs in biomedicine. Volume 143(2017)
• Page Start: 67
• Page End: 74
• Publication Date: 2017-05
• Subjects: Multiple supervised networks -- Osteosarcoma segmentation -- Convolutional neural networks; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.02.013
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: It is a deep end-to-end network for medical image segmentation. Multiple supervision side output layers were introduced to the network for guiding the multi-scale feature learning. A large number of feature channels were used in the up-sampling portion in order to capture more context information. The segmentation method achieved an average DSC of 87.80%, an average sensitivity of 86.88%, an average HM of 19.81%, and an F1-measure of 0.9080, these results are better than some existing studies. 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.