# A Dual-Stream Regulated Reconstruction and Segmentation Network with Hierarchical Artifact-Prior Modeling for Ultra-Low-Field Pediatric Neuroimaging

arXiv: https://arxiv.org/abs/2609.20562 · HTML: https://mlsanity.com/paper/2609.20562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [BIAS: Transparent reporting of biomedical image analysis challenges](https://mlsanity.com/paper/1910.04071/) (2019)
- [MELAGE: A purely python based Neuroimaging software (Neonatal)](https://mlsanity.com/paper/2309.07175/) (2023)
- [MGA-Net: A Novel Mask-Guided Attention Neural Network for Precision Neonatal Brain Imaging](https://mlsanity.com/paper/2406.17709/) (2024)
- [BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation](https://mlsanity.com/paper/2511.19394/) (2025)
- [Segmenting infant brains across magnetic fields: Domain randomization and annotation curation in ultra-low field MRI](https://mlsanity.com/paper/2510.17436/) (2025)

## Abstract (arXiv)

Automated quality assessment, enhancement, and segmentation of multiple structures in 0.064,T ultra-low-field pediatric MRI are limited by a low signal-to-noise ratio, weak anatomical boundaries, and frequent artifacts. We present a unified framework for the LISA 2026 Challenge that performs all three tasks together within one inference pipeline. A network with two coupled streams, built on a 3D U-Net, first reconstructs an enhanced uLF volume and then combines the original and enhanced images for subcortical segmentation. To improve boundary stability, we add an auxiliary class covering brain tissue outside the target structures, derived from whole brain masks. A head conditioned on an artifact graph predicts the seven artifact ratings from reconstruction residuals and frozen segmentation features. We address the scarcity of dense annotations using diffeomorphic registration from atlas to target for label propagation and to regularize anatomical reconstruction. We report validation results across all three tasks.