Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, local-
ization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of
objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we
propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape
segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical
shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation
approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima
optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter
estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution
by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We
evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained
a mean Dice similarity coefficient of 0:927 using only the four highest modes of variation (compared to 0:888 with classical
ASM1 (p-value=0:01) using same configuration). To the best of our knowledge this is the first demonstration of using DL
framework for parametrized shape learning for the delineation of deformable objects.
Awais Mansoor, Juan J. Cerrolaza, Geovany Perez, Elijah Biggs, Gustavo Nino, and Marius George Linguraru, "Marginal shape deep learning: applications to pediatric lung field segmentation," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013304 (Presented at SPIE Medical Imaging: February 12, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254412.
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