2024-12-23-omidi24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Unsupervised Domain Adaptation of Brain MRI Skull Stripping Trained on Adult Data to Newborns: Combining Synthetic Data with Domain Invariant Features	Skull-stripping constitutes a crucial initial step in neuroimaging analysis, and supervised deep-learning models have demonstrated considerable success in automating this task. However, a notable challenge is the limited availability of publicly accessible newborn brain MRI datasets. Furthermore, these datasets frequently use diverse post-processing techniques to improve image quality, which may not be consistently feasible in all clinical settings. Additionally, manual segmentation of newborn brain MR images is labor-intensive and demands specialized expertise, rendering it inefficient. While various adult brain MRI datasets with skull-stripping masks are publicly available, applying supervised models trained on these datasets directly to newborns poses a challenge due to domain shift. We propose a methodology that combines domain adversarial models to learn domain-invariant features between newborn and adult data, along with the integration of synthetic data generated using a Gaussian Mixture Model (GMM) as well as data augmentation procedures. The GMM method facilitates the creation of synthetic brain MR images, ensuring a diverse and representative input from multiple domains within our source dataset during model training. The data augmentation procedures were tailored to make the adult MRI data distribution closer to the newborn data distribution. Our results yielded an overall Dice coefficient of 0.9308 ± 0.0297 (mean± std), outperforming all compared unsupervised domain adaptation models and surpassing some supervised techniques previously trained on newborn data. This projectś code and trained models\’{weights} are publicly available at https://github.com/abbasomidi77/GMM-Enhanced-DAUnet	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	omidi24a	0	Unsupervised Domain Adaptation of Brain MRI Skull Stripping Trained on Adult Data to Newborns: Combining Synthetic Data with Domain Invariant Features	1073	1085	1073-1085	1073	false	Omidi, Abbas and Shamaei, Amirmohammad and Verschuu, Anouk and King, Regan and Leijser, Lara and Souza, Roberto	given family Abbas Omidi given family Amirmohammad Shamaei given family Anouk Verschuu given family Regan King given family Lara Leijser given family Roberto Souza	2024-12-23		Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning	250	inproceedings	date-parts 2024 12 23	https://raw.githubusercontent.com/mlresearch/v250/main/assets/omidi24a/omidi24a.pdf