Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models

TL;DR

This paper introduces Progressive DeepSSM, a multiscale training strategy for image-to-shape deep models that improves accuracy and stability in anatomical shape modeling from medical images.

Contribution

It proposes a novel multiscale training methodology inspired by multiresolution learning, enhancing deep shape models with shape priors and deep supervision.

Findings

Models trained with the proposed strategy outperform existing methods.

The approach improves training stability and model accuracy.

Qualitative and quantitative evaluations confirm the effectiveness.

Abstract

Statistical shape modeling (SSM) is an enabling quantitative tool to study anatomical shapes in various medical applications. However, directly using 3D images in these applications still has a long way to go. Recent deep learning methods have paved the way for reducing the substantial preprocessing steps to construct SSMs directly from unsegmented images. Nevertheless, the performance of these models is not up to the mark. Inspired by multiscale/multiresolution learning, we propose a new training strategy, progressive DeepSSM, to train image-to-shape deep learning models. The training is performed in multiple scales, and each scale utilizes the output from the previous scale. This strategy enables the model to learn coarse shape features in the first scales and gradually learn detailed fine shape features in the later scales. We leverage shape priors via segmentation-guided multi-task learning and employ deep supervision loss to ensure learning at each scale. Experiments show the superiority of models trained by the proposed strategy from both quantitative and qualitative perspectives. This training methodology can be employed to improve the stability and accuracy of any deep learning method for inferring statistical representations of anatomies from medical images and can be adopted by existing deep learning methods to improve model accuracy and training stability.