Progressive Learning Based Knowledge Distillation for Low Resolution Cerebral Microbleed Segmentation

Abstract

This study aims to address key technical issues in the segmentation of Cerebral MicroBleeds (CMBs) based on Low-Resolution (LR) Magnetic Resonance Imaging (MRI) data. There are two challenges in this task. First, the CMB lesions are typically small in size and easily confused with various mimics. Second, anisotropy becomes more prominent and adverse in LR MRI sequences than HR sequences. To address these issues, we propose a Progressive Learning based Knowledge Distillation method. This method progressively transfers knowledge from HR models to their LR counterparts, thereby minimizing the occurrence of false positives attributable to noise from Super-Resolution. To further eliminate the influence of anisotropy, an encoding-enhanced network, called E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> U-Net, is proposed in this paper. It can effectively capture anisotropic information and mitigates potential feature loss. The experimental results on multiple publicly accessible CMBs datasets demonstrated the superiority of our proposed approach over existing deep-learning methods.