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Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts

2022-09-20 13:41:16
Carles Garcia-Cabrera, Eric Arazo, Kathleen M. Curran, Noel E. O'Connor, Kevin McGuinness
arXiv_CV
         
Abstract

Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues. While there has been significant efforts on improving the quality of the algorithms, few works have tackled the harm that the artifacts generate in the predictions. In this work, we study fine tuning of pretrained networks to improve the resilience of previous methods to these artifacts. In our proposed method, we adopted the extensive usage of data augmentations that mimic those artifacts. The results significantly improved the baseline segmentations (up to 0.06 Dice score, and 4mm Hausdorff distance improvement).

Abstract (translated)

URL

https://arxiv.org/abs/2209.09714

PDF

https://arxiv.org/pdf/2209.09714.pdf

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