Joint PVL Detection and Manual Ability Classification Using Semi-supervised Multi-task Learning

Jingyun Yang, Jie Hu, Yicong Li, Heng Liu, Yang Li

Abstract: Among symptoms of cerebral palsy (CP), the degree of hand function impairment in young children is hard to assess due to large inter-personal variability and differences in evaluators’ experience. To help design better treatment strategies, accurate identification and delineation of manual ability injury level is a major clinical concern. Periventricular leukomalacia (PVL), a form of brain lesion in periventriular white matter in premature infants, is a leading cause of CP and have clinical associations with motor function injuries. In this paper, we exploit the correlation between PVL lesion segmentation and manual ability classification (MAC) to improve the identification performance of both tasks for T2 FLAIR MRI scans. Particularly, we propose a semi-supervised multi-task learning framework to jointly learn from heterogeneous datasets. Two clinically related auxiliary tasks, lesion localization and ventricle segmentation, are also incorporated to improve the classification accuracy while requiring only a small amount of manual annotations. Using two datasets containing 24 labeled PVL samples and 87 labeled MAC samples, the proposed model significantly outperforms single-task methods, achieving a dice score of 0.607 for PVL lesion segmentation and 84.3% accuracy for manual ability classification.