Dynamic characterisation of Meibomian gland structure

Student thesis: Phd


Current methods of Meibomian gland imaging present analysis at a single point in time, with the assumption that gland structure changes very slowly. Despite the importance of meibography in Meibomian gland dysfunction diagnosis, management, and treatment there is still poor evidence on whether gland structure or the presence of functional acini is demonstrated in meibography images. Meibography is a fully objective technique, however the image interpretation remains subjective and unclear. This thesis reports on work to enhance our understanding of Meibomian gland physiology via the development of improved imaging systems, image analysis software and clinical studies evaluating change in gland appearance over time. The first clinical study reports on diurnal and monthly variations of visible Meibomian gland structure. The findings revealed some variation over time when glands are being analysed individually. To further investigate changes in Meibomian gland appearance, the second clinical study explored how Meibomian glands change after therapeutic expression. It was shown that Meibomian glands get dimmer and shorter following therapeutic expression but recover within 24 hours meaning that they are capable of 'regeneration' in a relatively short period of time. Development of custom imaging system and image analysis software helped capture and analyse images in a reliable way. The results presented in this thesis add to our understanding of individual Meibomian gland function in a young and healthy population. This information is essential to fully understand the complicated nature of holocrine glands by providing insights into whether meibography captures gland structure or the presence of functional acini. Furthermore, the reported deep learning-based algorithm can be utilised in Meibomian gland health assessment by facilitating clinicians in Meibomian gland dysfunction diagnosis, management and treatment.
Date of Award1 Aug 2023
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorPhilip Morgan (Supervisor), Carole Maldonado-Codina (Supervisor), Michael Read (Supervisor) & Martin Fergie (Supervisor)


  • dry eye
  • image processing
  • artificial intelligence
  • deep learning
  • meibography
  • Meibomian glands

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