Automatic markup of neural cell membranes using boosted decision stumps

they need to reconstruct the underlying neural circuitry. The neural circuit, which consists of the neuron cells and synapses in a three-dimensional (3D) volume of tissue are scanned slice-by-slice at very high magnifications using an electron microscope. From the electron microscopy images, the neurons and their connections (synapses) are identified to lay out the connections of the neural circuitry. One of the necessary tasks in this process is to segment the individual neurons in the images of the sliced volume. To effectively carry out this segmentation we need to delineate the cell membranes of the neurons. For this purpose, we propose a supervised learning approach to detect the cell membranes. The classifier was trained using decision stumps boosted using AdaBoost, on local and context features. The features were selected to highlight the curve like characteristics of cell membranes. It is also shown that using features from context positions allows for more information to be utilized in the classification. Together with the nonlinear discrimination ability of the AdaBoost classifier there are clearly noticeable improvements over previously used methods. We also detail several experiments conducted for identification of synapse structures in the microscopy images.