Probabilistic segmentation of small metastatic brain tumors using liquid state machine ensemble

Segmenting small brain tumors (diameter ≤ 0.5 cm) on contrast enhanced MRI images presents a particular problem, as enhancing blood vessels of similar size can be detected as false positives. The capabilities of Liquid State Machines (LSM) ensembles to separate high dimensional data are used in this project to overcome this problem. Contrast enhanced MRI images were first transformed into time series before being fed into the LSM, which consists of a 3 dimensional array of spiking neurons, the resulting activation patterns of both the excitatory and inhibitory neurons differed from each other to a high enough degree that enhancing tumors and blood vessel of similar size could be distinguished from one another. An ensemble of two LSM's, which differed in the way the time series information was input was used to enhance data separation. The combined output of the LSM ensemble was then used as input into a random forest to classify the final result as tumor vs. non-tumor. In comparison with deep learning CNN our results show excellent small tumor recognition and generate probability maps that cover the tumors but ignore blood vessels and other contrast-enhancing objects.