# Reference-Free 3D Reconstruction of Brain Dissection Slabs via Learned Atlas Coordinates

arXiv: https://arxiv.org/abs/2503.09963 · HTML: https://mlsanity.com/paper/2503.09963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [SynthMorph: learning contrast-invariant registration without acquired images](https://mlsanity.com/paper/2004.10282/) (2020)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach](https://mlsanity.com/paper/1703.10908/) (2017)
- [End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network](https://mlsanity.com/paper/1704.06065/) (2017)
- [uniGradICON: A Foundation Model for Medical Image Registration](https://mlsanity.com/paper/2403.05780/) (2024)
- [Multi-Atlas Segmentation of Biomedical Images: A Survey](https://mlsanity.com/paper/1412.3421/) (2014)
- [Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets](https://mlsanity.com/paper/2209.02032/) (2022)
- [textttGradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency](https://mlsanity.com/paper/2206.05897/) (2022)
- [Slice-to-volume medical image registration: a survey](https://mlsanity.com/paper/1702.01636/) (2017)
- [Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging](https://mlsanity.com/paper/2311.16914/) (2023)
- [Registration by Regression (RbR): a framework for interpretable and flexible atlas registration](https://mlsanity.com/paper/2404.16781/) (2024)
- [3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology](https://mlsanity.com/paper/2009.05596/) (2020)

## Abstract (arXiv)

Correlation of neuropathology with MRI has the potential to transfer microscopic signatures of pathology to in vivo scans. There is increasing interest in building these correlations from 3D reconstructed stacks of slab photographs, which are routinely taken during dissection at brain banks. These photographs bypass the need for ex vivo MRI, which is not widely accessible. However, existing methods either require a corresponding 3D reference (e.g., an ex vivo MRI scans, or a brain surface acquired with a structured light scanner) or a full stack of brain slabs, which severely limits applicability. Here we propose RefFree, a 3D reconstruction method for dissection photographs that does not require an external reference. RefFree coherently reconstructs a 3D volume for an arbitrary set of slabs (including a single slab) using predicted 3D coordinates in the standard atlas space (MNI) as guidance. To support RefFree's pipeline, we train an atlas coordinate prediction network that estimates the coordinate map from a 2D photograph, using synthetic photographs generated from digitally sliced 3D MRI data with randomized appearance for enhanced generalization. As a by-product, RefFree can propagate information (e.g., anatomical labels) from atlas space to one single photograph even without reconstruction. Experiments on simulated and real data show that, when all slabs are available, RefFree achieves performance comparable to existing classical methods but at substantially higher speed. Moreover, RefFree yields accurate reconstruction and registration for partial stacks or even a single slab. Our code is available at https://github.com/lintian-a/reffree.