Performance Evaluation of Vanilla, Residual, and Dense 2D U-Net Architectures for Skull Stripping of Augmented 3D T1-weighted MRI Head Scans

Autor:	Pimpalkar, Anway S., Patole, Rashmika K., Kamble, Ketaki D., Shindikar, Mahesh H.
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Skull Stripping is a requisite preliminary step in most diagnostic neuroimaging applications. Manual Skull Stripping methods define the gold standard for the domain but are time-consuming and challenging to integrate into processing pipelines with a high number of data samples. Automated methods are an active area of research for head MRI segmentation, especially deep learning methods such as U-Net architecture implementations. This study compares Vanilla, Residual, and Dense 2D U-Net architectures for Skull Stripping. The Dense 2D U-Net architecture outperforms the Vanilla and Residual counterparts by achieving an accuracy of 99.75% on a test dataset. It is observed that dense interconnections in a U-Net encourage feature reuse across layers of the architecture and allow for shallower models with the strengths of a deeper network. Comment: Research Article submitted to the 2nd International Conference on Biomedical Engineering Science and Technology: Roadway from Laboratory to Market, at the National Institute of Technology Raipur, Chhattisgarh, India
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.16570 Zobrazit plný text záznamu View this record from Arxiv