DeepAI AI Chat
Log In Sign Up

DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images

by   Riddhish Bhalodia, et al.

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.


page 7

page 8

page 10


Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions

Statistical shape modeling (SSM) is an essential tool for analyzing vari...

Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor

In current biological and medical research, statistical shape modeling (...

DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models

Statistical shape modeling (SSM) characterizes anatomical variations in ...

Self-Supervised Discovery of Anatomical Shape Landmarks

Statistical shape analysis is a very useful tool in a wide range of medi...

Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model

Statistical shape modeling is an essential tool for the quantitative ana...

Uncertain-DeepSSM: From Images to Probabilistic Shape Models

Statistical shape modeling (SSM) has recently taken advantage of advance...

End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images

We present an end-to-end Convolutional Neural Network (CNN) approach for...