# Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas

arXiv: https://arxiv.org/abs/2501.16879 · HTML: https://mlsanity.com/paper/2501.16879/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 61 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation](https://mlsanity.com/paper/1911.09098/) (2019)
- [Deep ICE: A Deep learning approach for MRI Intracranial Cavity Extraction](https://mlsanity.com/paper/2001.05720/) (2020)
- [DeepCERES: A Deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI](https://mlsanity.com/paper/2401.12074/) (2024)
- [vol2Brain: A new online Pipeline for whole Brain MRI analysis](https://mlsanity.com/paper/2202.03920/) (2022)

## Abstract (arXiv)

In this paper, we introduce a novel structural holistic Atlas (holiAtlas) of the human brain anatomy based on multimodal and high-resolution MRI that covers several anatomical levels from the organ to the substructure level, using a new densely labelled protocol generated from the fusion of multiple local protocols at different scales. This atlas was constructed by averaging images and segmentations of 75 healthy subjects from the Human Connectome Project database. Specifically, MR images of T1, T2 and WMn (White Matter nulled) contrasts at 0.125 mm3 resolution were selected for this project. The images of these 75 subjects were nonlinearly registered and averaged using symmetric group-wise normalisation to construct the atlas. At the finest level, the proposed atlas has 350 different labels derived from 7 distinct delineation protocols. These labels were grouped at multiple scales, offering a coherent and consistent holistic representation of the brain across different levels of detail. This multiscale and multimodal atlas can be used to develop new ultra-high-resolution segmentation methods, potentially improving the early detection of neurological disorders. We make it publicly available to the scientific community.