Eman Alajrami, Tiffany Ng, Jevgeni Jevsikov, Preshen Naidoo, Patricia Fernandes, Neda Azarmehr, Fateme Dinmohammadi, Matthew J Shun-Shin, Nasim Dadashi Serej, Darrel P Francis, Massoud Zolgharni
BACKGROUND AND OBJECTIVE: Training deep learning models for medical image segmentation require large annotated datasets, which can be expensive and time-consuming to create. Active learning is a promising approach to reduce this burden by strategically selecting the most informative samples for segmentation. This study investigates the use of active learning for efficient left ventricle segmentation in echocardiography with sparse expert annotations. METHODS: We adapt and evaluate various sampling techniques, demonstrating their effectiveness in judiciously selecting samples for segmentation...
March 7, 2024: Computer Methods and Programs in Biomedicine