Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts

Autor:	Garcia-Cabrera, Carles, Arazo, Eric, Curran, Kathleen M., O'Connor, Noel E., McGuinness, Kevin
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:	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). Comment: accepted for the STACOM2022 workshop @ MICCAI2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.09714 Zobrazit plný text záznamu View this record from Arxiv