Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models
Abu Zahid Bin Aziz Jadie Adams Shireen Elhabian
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.