Neurodegenerative diseases are a group of largely incurable disorders characterised by the progressive loss of neurons and for which often the molecular mechanisms are poorly understood. To bridge this gap, researchers employ a range of techniques. A very prominent and useful technique adopted across many different fields is imaging and the analysis of histopathological and fluorescent label tissue samples. Although image acquisition has been efficiently automated recently, automated analysis still presents a bottleneck. Although various methods have been developed to automate this task, they tend to make use of single-purpose machine learning models that require extensive training, imposing a significant workload on the experts and introducing variability in the analysis. Moreover, these methods are impractical to audit and adapt, as their internal parameters are difficult to interpret and change. Here, we present a novel unsupervised automated schema for object segmentation of images, exemplified on a dataset of tissue images. Our schema does not require training data, can be fully audited and is based on a series of understandable biological decisions. In order to evaluate and validate our schema, we compared it with a state-of-the-art automated segmentation method for post-mortem tissues of ALS patients.
Bibliographical noteKAUST Repository Item: Exported on 2021-07-14
Acknowledgements: The authors gratefully acknowledge the financial support of the Spanish Ministry of Science (Project PID2019-108392GB-I00 AEI/FEDER, UE), the funding from the European Union’s H2020 research and innovation programme under Marie Sklodowska-Curie Grant Agreement Number 801586, as well as that of Navarra de Servicios y Tecnologías, S.A. (NASERTIC). A.S. and C.H. wish to acknowledge the support of King’s College London (Studentship “LAMBDA: long axons for motor neurons in a bioengineered model of ALS” from FoDOCS) and the Wellcome Trust (213949/Z/18/Z). On behalf of A.S., C.H. and R.P., this research was funded in whole, or in part, by the Wellcome Trust (213949/Z/18/Z). For the purposes of open access, the authorhas applied a CC BY public copyright licence to any author-accepted manuscript version arising from this submission.