High-accuracy neurite reconstruction for high-throughput neuroanatomy

Moritz Helmstaedter (1) , Kevin L Briggman (1) , Winfried Denk (1)

1 Biomedical Optics

Neuroanatomic analysis depends on the reconstruction of complete cell shapes. High-throughput reconstruction of neural circuits ("connectomics") using volume electron microscopy requires dense staining of all cells, where even experts make annotation errors. Currently, reconstruction rather than acquisition speed limits the determination of neural wiring diagrams. We present methods for the fast and reliable reconstruction of densely labeled datasets. Our approach, based on manually skeletonizing each neurite redundantly (multiple times) with a special visualization/annotation software tool (KNOSSOS), is ~50 times faster than volume labeling. Errors are detected and eliminated by a "redundant-skeleton consensus procedure" (RESCOP), which uses a statistical model of how true neurite connectivity is transformed into annotation decisions. RESCOP also estimates the consensus skeletons' reliability. Focused re-annotation of difficult locations promises a rather steep increase of reliability as a function of the average skeleton redundancy and thus the nearly error-free analysis of large neuroanatomical datasets.

Mots clés: Technical report

Moritz Helmstaedter, Kevin L Briggman, Winfried Denk. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nature Neuroscience, 2011, ⟨10.1038/nn.2868⟩. ⟨hal-00658165⟩