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  3. Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Department of Brain and Cognitive Sciences (BCS)
Brain Lunch

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection

Speaker(s)
Kisuk Lee - Seung Lab
Add to CalendarAmerica/New_YorkRecursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection11/30/2015 5:00 pm11/30/2015 6:00 pmBrain and Cognitive Sciences Complex, 43 Vassar Street, McGovern Seminar Room 46-3189, Cambridge MA
November 30, 2015
5:00 pm - 6:00 pm
Location
Brain and Cognitive Sciences Complex, 43 Vassar Street, McGovern Seminar Room 46-3189, Cambridge MA
Contact
Julianne Gale
    Description

    Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics. An important computation for reconstruction is the detection of neuronal boundaries. Images acquired by serial section EM, a leading 3D EM technique, are highly anisotropic, with inferior quality along the third dimension. For such images, the 2D max-pooling convolutional network has set the standard for performance at boundary detection. Here we achieve a substantial gain in accuracy through three innovations. Following the trend towards deeper networks for object recognition, we use a much deeper network than

    previously employed for boundary detection. Second, we incorporate 3D as well as 2D filters, to enable computations that use 3D context. Finally, we adopt a recursively trained architecture in which a first network generates a preliminary boundary map that is provided as input along

    with the original image to a second network that generates a final boundary map. Backpropagation training is accelerated by ZNN, a new implementation of 3D convolutional networks that uses multicore CPU parallelism for speed. Our hybrid 2D-3D architecture could be more generally applicable to other types of anisotropic 3D images, including video, and our recursive framework for any image labeling problem.

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