# Deep ICE: A Deep learning approach for MRI Intracranial Cavity Extraction

arXiv: https://arxiv.org/abs/2001.05720 · HTML: https://mlsanity.com/paper/2001.05720/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [DeepCERES: A Deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI](https://mlsanity.com/paper/2401.12074/) (2024)
- [DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI](https://mlsanity.com/paper/2401.07751/) (2024)
- [Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas](https://mlsanity.com/paper/2501.16879/) (2025)
- [petBrain: A New Pipeline for Amyloid, Tau Tangles and Neurodegeneration Quantification Using PET and MRI](https://mlsanity.com/paper/2506.03217/) (2025)

## Abstract (arXiv)

Automatic methods for measuring normalized regional brain volumes from MRI data are a key tool to help in the objective diagnostic and follow-up of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume is commonly used for normalization. In this paper, we present an accurate and efficient approach to automatically segment the intracranial cavity using a volumetric 3D convolutional neural network and a new 3D patch extraction strategy specially adapted to deal with the traditional low number of training cases available in supervised segmentation and the memory limitations of modern GPUs. The proposed method is compared with recent state-of-the-art methods and the results show an excellent accuracy and improved performance in terms of computational burden.