Non-invasive spleen volume estimation is essential in detecting splenomegaly. Magnetic resonance imaging (MRI) has
been used to facilitate splenomegaly diagnosis in vivo. However, achieving accurate spleen volume estimation from MR
images is challenging given the great inter-subject variance of human abdomens and wide variety of clinical
images/modalities. Multi-atlas segmentation has been shown to be a promising approach to handle heterogeneous data and
difficult anatomical scenarios. In this paper, we propose to use multi-atlas segmentation frameworks for MRI spleen
segmentation for splenomegaly. To the best of our knowledge, this is the first work that integrates multi-atlas segmentation
for splenomegaly as seen on MRI. To address the particular concerns of spleen MRI, automated and novel semi-automated
atlas selection approaches are introduced. The automated approach interactively selects a subset of atlases using selective
and iterative method for performance level estimation (SIMPLE) approach. To further control the outliers, semi-automated
craniocaudal length based SIMPLE atlas selection (L-SIMPLE) is proposed to introduce a spatial prior in a fashion to
guide the iterative atlas selection. A dataset from a clinical trial containing 55 MRI volumes (28 T1 weighted and 27 T2
weighted) was used to evaluate different methods. Both automated and semi-automated methods achieved median DSC >
0.9. The outliers were alleviated by the L-SIMPLE (≈1 min manual efforts per scan), which achieved 0.9713 Pearson
correlation compared with the manual segmentation. The results demonstrated that the multi-atlas segmentation is able to
achieve accurate spleen segmentation from the multi-contrast splenomegaly MRI scans.
Yuankai Huo, Jiaqi Liu, Zhoubing Xu, Robert L. Harrigan, Albert Assad, Richard G. Abramson, and Bennett A. Landman, "Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330A (Presented at SPIE Medical Imaging: February 12, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254147.
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