DeepAI AI Chat
Log In Sign Up

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

by   Riddhish Bhalodia, et al.

Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. SSM requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, re-sampling, registration, and non-linear optimization. These shape representations are then used to extract low-dimensional shape descriptors that facilitate subsequent analyses in different applications. However, the current process of obtaining these shape descriptors from imaging data relies on human and computational resources, requiring domain expertise for segmenting anatomies of interest. Moreover, this same taxing pipeline needs to be repeated to infer shape descriptors for new image data using a pre-trained/existing shape model. Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images. Once trained using an existing shape model, DeepSSM circumvents the heavy and manual pre-processing and segmentation and significantly improves the computational time, making it a viable solution for fully end-to-end SSM applications. In addition, we introduce a model-based data-augmentation strategy to address data scarcity. Finally, this paper presents and analyzes two different architectural variants of DeepSSM with different loss functions using three medical datasets and their downstream clinical application. Experiments showcase that DeepSSM performs comparably or better to the state-of-the-art SSM both quantitatively and on application-driven downstream tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep learning-based image-to-shape models.


page 9

page 12

page 14

page 18

page 19

page 20

page 21


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

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

Uncertain-DeepSSM: From Images to Probabilistic Shape Models

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

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

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

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...

From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach

Statistical shape modeling (SSM) directly from 3D medical images is an u...

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

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

Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor

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