Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
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 maxpooling 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.
The paper proposed to use a composition of a ConvNet with 2D filters and a ConvNet with 3D filters to improve the quality of neural boundary prediction. The authors motivate their architectural choice by the anisotropy (x-y vs z) of the sequential EM scans often used in Neuroscience. Their methods clearly outperforms the previous method (only using 2D filters) on this task which is important for their application.The quality of the presentation could be easily improved and presentation could be streamlined. The method employs previously suggested techniques such as the recursive (composition) of nets and the application of 3D filters for 3D data sets.The paper has unfortunately some problems. The paper refers often to the implementation ZNN (apparently theirs) that is mentioned to be crucial to get it efficient on the size of the network and the amount of data. However, there is no reference provided nor sufficient details are provided to reproduce it. I suggest to actually provide concise details on the essential parts of the implementation and also on whether the software will be released in some way. Otherwise the paper is full of general design choices used to develop ZNN that are not precise enough. The captions to the figures should be more descriptive, as important information is missing to understand them.Details: l80: no reference for ZNN is provided. The reader has no idea what ZNN is. Is it an algorithm or a particular implementation? l177: the description of the Rand index is not sufficiently clear. There is a reference, but the paper should be self-consistent. Please provide the necessary formula. Section 3: This section does not really help to understand the paper. ZNN is not described in enough detail to reproduce the results. I suggest to give more precise details, or provide a reference to the resource where details can be found. Figure 3: color code? l393: In Fig 1 and the rest of the paper you have not used net1 and net2, please be consistent l396: As a comment: recursive seems not to be the right term for because it is not the same network that is recursively applied. It is the composition if vd2d an vd2d3d, although they share the same first layers. Is the vd2d part actually the same or just copied after the initial training and then refined independently?