# Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

## TL;DR

This paper investigates how transfer learning can be effectively applied to adapt CNN models for brain lesion segmentation across different MRI protocols, focusing on data requirements and parameter tuning for domain adaptation.

## Contribution

It provides insights into the minimal data needed and which model parameters to retrain for effective domain adaptation in MRI segmentation tasks.

## Key findings

- Domain-adapted network with two examples achieved Dice score of 0.63
- Transfer learning outperforms training from scratch with limited data
- Effective parameter tuning enhances cross-domain MRI segmentation

## Abstract

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.