Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume.

Background: Notch volume is associated with anterior cruciate ligament (ACL) injury. Manual tracking of intercondylar notch on MR images is time-consuming and laborious. Deep learning has become a powerful tool for processing medical images.

Improving sensitivity and connectivity of retinal vessel segmentation via error discrimination network.

Purpose: Automated retinal vessel segmentation is crucial to the early diagnosis and treatment of ophthalmological diseases. Many deep-learning-based methods have shown exceptional success in this task. However, current approaches are still inadequate in challenging vessels (e.

Identity-mapping cascaded network for fMRI registration.

Neuroscience researches based on functional magnetic resonance imaging (fMRI) rely on accurate inter-subject image registration of functional regions. The intersubject alignment of fMRI can improve the statistical power of group analyses. Recent studies have shown the deep learning-based registration methods can be used for registration.