Boosting Skull-Stripping Performance for Pediatric Brain Images

Abstract

Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.

arXiv comment5 pages, 5 figures, 1 table, skull-stripping, brain extraction, newborn, infant, toddler, pediatric MRI, machine learning, accepted by the IEEE International Symposium on Biomedical Imaging

References in corpus (8)

• Automated brain extraction of multi-sequence MRI using artificial neural networks
• SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
• Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
• Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years
• Learning the Effect of Registration Hyperparameters with HyperMorph
• Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
• Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast
• Fast Infant MRI Skullstripping with Multiview 2D Convolutional Neural Networks

Cited by in corpus (4)

• Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
• Domain-randomized deep learning for neuroimage analysis
• Deep infant brain segmentation from multi-contrast MRI
• SingleStrip: learning skull-stripping from a single labeled example