# March 1, 2011 @ 1130am: Srini Turaga (MIT)

**End-to-end machine learning of image segmentation
(for neural circuit reconstruction)**

Srinivas Turaga (MIT)

Supervised machine learning is a powerful tool for creating image segmentation algorithms that are well adapted to our datasets. Such algorithms have three basic components: 1) a parametrized function for producing segmentations from images, 2) an objective function that quantifies the performance of a segmentation algorithm relative to ground truth, and 3) a means of searching the parameter space of the segmentation algorithms for an optimum of the objective function.

In this talk, I will present new work in each of these areas: 1) a segmentation algorithm based on convolutional networks as boundary detectors, 2) the Rand index as a measure of segmentation quality, and 3) the MALIS algorithm for training boundary detectors to optimize the Rand index segmentation measure. Taken together, these three pieces constitute the first system for truly “end-to-end” learning of image segmentation, where all parameters in the algorithm are adjusted to directly minimize segmentation error.