Authors: Riddhish Bhalodia,Shireen Y. Elhabian,Ladislav Kavan,Ross T. Whitaker
ArXiv: 1810.00111
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DOI
Abstract URL: http://arxiv.org/abs/1810.00111v1
Statistical shape modeling is an important tool to characterize variation in
anatomical morphology. Typical shapes of interest are measured using 3D imaging
and a subsequent pipeline of registration, segmentation, and some extraction of
shape features or projections onto some lower-dimensional shape space, which
facilitates subsequent statistical analysis. Many methods for constructing
compact shape representations have been proposed, but are often impractical due
to the sequence of image preprocessing operations, which involve significant
parameter tuning, manual delineation, and/or quality control by the users. We
propose DeepSSM: a deep learning approach to extract a low-dimensional shape
representation directly from 3D images, requiring virtually no parameter tuning
or user assistance. DeepSSM uses a convolutional neural network (CNN) that
simultaneously localizes the biological structure of interest, establishes
correspondences, and projects these points onto a low-dimensional shape
representation in the form of PCA loadings within a point distribution model.
To overcome the challenge of the limited availability of training images, we
present a novel data augmentation procedure that uses existing correspondences
on a relatively small set of processed images with shape statistics to create
plausible training samples with known shape parameters. Hence, we leverage the
limited CT/MRI scans (40-50) into thousands of images needed to train a CNN.
After the training, the CNN automatically produces accurate low-dimensional
shape representations for unseen images. We validate DeepSSM for three
different applications pertaining to modeling pediatric cranial CT for
characterization of metopic craniosynostosis, femur CT scans identifying
morphologic deformities of the hip due to femoroacetabular impingement, and
left atrium MRI scans for atrial fibrillation recurrence prediction.