Open AccessResearch on multi-path dense networks for MRI spinal segmentation
Author(s) -
Sheng Liang,
Huilin Liu,
Chen Chen,
Chuanbo Qin,
FangChen Yang,
Yue Feng,
Zhuosheng Lin
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0248303
Subject(s) - jaccard index , segmentation , computer science , artificial intelligence , pattern recognition (psychology) , feature (linguistics) , sørensen–dice coefficient , convolution (computer science) , feature extraction , enhanced data rates for gsm evolution , convolutional neural network , image segmentation , artificial neural network , philosophy , linguistics
Accurate and robust segmentation of anatomical structures from magnetic resonance images is valuable in many computer-aided clinical tasks. Traditional codec networks are not satisfactory because of their low accuracy of edge segmentation, the low recognition rate of the target, and loss of detailed information. To address these problems, this study proposes a series of improved models for semantic segmentation and progressively optimizes them from the three aspects of convolution module, codec unit, and feature fusion. Instead of the standard convolution structure, we apply a new type of convolution module for the feature extraction. The networks integrate a multi-path method to obtain richer-detail edge information. Finally, a dense network is utilized to strengthen the ability of the feature fusion and integrate more different-level information. The evaluation of the Accuracy, Dice coefficient, and Jaccard index led to values of 0.9855, 0.9185, and 0.8507, respectively. These metrics of the best network increased by 1.0%, 4.0%, and 6.1%, respectively. Boundary F1-Score reached 0.9124 indicating that the proposed networks can segment smaller targets to obtain smoother edges. Our methods obtain more key information than traditional methods and achieve superiority in segmentation performance.