# FRNET: Flattened Residual Network for Infant MRI Skull Stripping

## TL;DR

This paper introduces FRNET, a robust CNN-based framework with a novel boundary loss and data augmentation for infant MRI skull stripping, outperforming existing methods across various ages.

## Contribution

The paper presents a new flattened residual network architecture and a boundary loss function specifically designed for infant MRI skull stripping, addressing challenges of small size and low contrast.

## Key findings

- FRNET outperforms state-of-the-art methods in all age groups.
- The boundary loss improves segmentation accuracy in ambiguous regions.
- Data augmentation with artifact simulation enhances robustness.

## Abstract

Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.