#771 AQUARED DEEP DREAMER SERIAL#
#771 AQUARED DEEP DREAMER MANUAL#
#771 AQUARED DEEP DREAMER SOFTWARE#
#771 AQUARED DEEP DREAMER PLUS#

The connectomic image processing system of Fig. Both ssEM methods produce images that can be reconstructed by very similar algorithms, and we will typically not distinguish between them in the following.

#771 AQUARED DEEP DREAMER SERIAL#

Serial section TEM and SEM are more similar to each other, and more different from bfEM techniques. More recently, serial sections have also been imaged by scanning electron microscopy (SEM). Serial section EM was originally done using transmission electron microscopy (TEM). This will likely determine whether ssEM will remain popular, or be eclipsed by bfEM. The question for the future is whether AI will be able to robustly handle the deficiencies of ssEM images. At present ssEM remains the most widely used technique for neural circuit reconstruction, judging from number of publications.

This review will focus on ssEM image analysis. Gray dashed lines indicate alternative paths taken by some methods. Error detection and correction is performed by convolutional nets or human proofreaders. Synaptic partners in the segmentation are assigned to synapses using convolutional nets or other machine learning algorithms. Supervoxels are agglomerated to produce segments using hand-designed heuristics or machine learning algorithms. The boundary map is oversegmented into supervoxels by a watershed-type algorithm. Neuronal boundaries and synaptic clefts are detected by convolutional nets. 1) now contains multiple convolutional nets performing a variety of image analysis tasks.Įxample of connectomic image processing system. The ideas were first put into practice in a system that applied a single convolutional net to convert manually traced neuronal skeletons into 3D reconstructions, and a semiautomated system (Eyewire) in which humans reconstructed neurons by interacting with a similar convolutional net. In this journal, our lab previously reviewed the application of convolutional nets to connectomics. The challenge of robustly handling edge cases is universal to building real-world AI systems, and makes the difference between a self-driving car for research and one that will be commercially successful.

This is currently its major shortcoming relative to human intelligence, and the toughest barrier to creating practical AI systems for ssEM images. But AI can fail catastrophically at image defects. ĪI is now highly accurate at analyzing ssEM images under “good conditions,” and continues to improve. For example, most self-driving cars currently employ an expensive LIDAR sensor with primitive navigation algorithms, but cheap video cameras with more advanced AI may turn out to be sufficient in the long run. The question of whether to invest in better data or better algorithms often arises in AI research and development. The future of ssEM depends on this possibility.

#771 AQUARED DEEP DREAMER PLUS#

But it is also possible that powerful AI plus lower quality ssEM images might be sufficient for delivering the accuracy that most neuroscientists need. It is possible that the entire field of connectomics will eventually switch to bfEM, following the lead of Janelia. Janelia has decided that this is overall the cheapest route to the fly connectome, even if FIB-SEM imaging is expensive (Gerry Rubin, personal communication).

#771 AQUARED DEEP DREAMER MANUAL#

FIB-SEM quality is expected to boost the accuracy of image analysis by AI, thereby reducing costly manual image analysis by humans. For its fly connectome project, Janelia Research Campus has invested heavily in FIB-SEM, a bfEM technique that delivers images with isotropic 8 nm voxels and few defects. Block face EM (bfEM) was originally introduced to deal with these problems. Images acquired by ssEM may contain many defects, such as damaged sections and misalignments, and axial resolution is poor. One example is the ongoing duel between 3D EM imaging approaches, serial section and block face. More specific impacts of AI on the technologies of connectomics are harder to predict. It now seems likely that the dream of connectomes with minimal human labor will eventually be realized with the aid of artificial intelligence (AI).

#771 AQUARED DEEP DREAMER SOFTWARE#

More recently, convolutional nets were finally accepted by mainstream computer vision, and enhanced by huge investments in hardware and software for deep learning. In the 2000s, connectomics was one of the first applications of convolutional nets to dense image prediction. The dream of automating EM image analysis dates back to the dawn of computer vision in the 1960s and 70s (Marvin Minsky, personal communication). Even recent ssEM reconstructions of neural circuits have required tens of thousands of hours of manual labor. elegans nervous system by serial section electron microscopy (ssEM) required years of laborious manual image analysis.

771 aquared deep dreamer