SegFormer3D: Improving the Robustness of Deep Learning Model-Based Image Segmentation in Ultrasound Volumes of the Pediatric Hip

Abstract

Developmental dysplasia of the hip (DDH) is a painful orthopedic malformation diagnosed at birth in 1–3% of all newborns. Left untreated, DDH can lead to significant morbidity including long-term disability. Currently the condition is clinically diagnosed using 2-D ultrasound (US) imaging acquired between 0 and 6 mo of age. DDH metrics are manually extracted by highly trained radiologists through manual measurements of relevant anatomy from the 2-D US data, which remains a time-consuming and highly error-prone process. Recently, it was shown that combining 3-D US imaging with deep learning-based automated diagnostic tools may significantly improve accuracy and reduce variability in measuring DDH metrics. However, the robustness of current techniques remains insufficient for reliable deployment into real-life clinical workflows. In this work, we first present a quantitative robustness evaluation of the state of the art in bone segmentation from 3-D US and demonstrate examples of failed or implausible segmentations with convolutional neural network and vision transformer models under common data variations, e.g., small changes in image resolution or anatomical field of view from those encountered in the training data. Second, we propose a 3-D extension of SegFormer architecture, a lightweight transformer-based model with hierarchically structured encoders producing multi-scale features, which we show to concurrently improve accuracy and robustness. Quantitative results on clinical data from pediatric patients in the test set showed up to 0.9% improvement in Dice score and up to a 3% smaller Hausdorff distance 95% compared with state of the art when unseen variations in anatomical structures and data resolutions were introduced.