Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor

by   Riddhish Bhalodia, et al.

In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation. Dense correspondence models can provide ease of computation and yield an interpretable low-dimensional shape descriptor when followed by dimensionality reduction. However, automatic methods for obtaining such correspondences usually require image segmentation followed by significant preprocessing, which is taxing in terms of both computation as well as human resources. In many cases, the segmentation and subsequent processing require manual guidance and anatomy specific domain expertise. This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis. We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well. We also propose a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain. The proposed method circumvents segmentation and preprocessing and directly produces a usable shape descriptor using just 2D or 3D images. In addition, we also propose two variants on the training loss function that allows for prior shape information to be integrated into the model. We apply this framework on several 2D and 3D datasets to obtain their shape descriptors, and analyze their utility for various applications.


page 5

page 8

page 12

page 14

page 16

page 18

page 19

page 22


Self-Supervised Discovery of Anatomical Shape Landmarks

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

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

Statistical shape modeling is an important tool to characterize variatio...

Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation

Left atrium shape has been shown to be an independent predictor of recur...

Learning Deep Features for Shape Correspondence with Domain Invariance

Correspondence-based shape models are key to various medical imaging app...

Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach

Clinical investigations of anatomy's structural changes over time could ...

Using compatible shape descriptor for lexicon reduction of printed Farsi subwords

This Paper presents a method for lexicon reduction of Printed Farsi subw...

S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences

Statistical shape models (SSMs) are an established way to geometrically ...

Please sign up or login with your details

Forgot password? Click here to reset