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Statistical shape modeling (SSM) is an enabling quantitative tool to studyanatomical shapes in various medical applications. However, directly using 3Dimages in these applications still has a long way to go. Recent deep learningmethods have paved the way for reducing the substantial preprocessing steps toconstruct SSMs directly from unsegmented images. Nevertheless, the performanceof these models is not up to the mark. Inspired by multiscale/multiresolutionlearning, we propose a new training strategy, progressive DeepSSM, to trainimage-to-shape deep learning models. The training is performed in multiplescales, and each scale utilizes the output from the previous scale. Thisstrategy enables the model to learn coarse shape features in the first scalesand gradually learn detailed fine shape features in the later scales. Weleverage shape priors via segmentation-guided multi-task learning and employdeep supervision loss to ensure learning at each scale. Experiments show thesuperiority of models trained by the proposed strategy from both quantitativeand qualitative perspectives. This training methodology can be employed toimprove the stability and accuracy of any deep learning method for inferringstatistical representations of anatomies from medical images and can be adoptedby existing deep learning methods to improve model accuracy and trainingstability.

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