N3DFix: an Algorithm for Automatic Removal of Swelling Artifacts in Neuronal Reconstructions

It is well established that not only electrophysiology but also morphology plays an important role in shaping the functional properties of neurons. In order to properly quantify morphological features it is first necessary to translate observational histological data into 3-dimensional geometric reconstructions of the neuronal structures. This reconstruction process, independently of being manual or (semi-)automatic, requires several preparation steps (e.g. histological processing) before data acquisition using specialized software. Unfortunately these processing steps likely produce artifacts which are then carried to the reconstruction, such as tissue shrinkage and formation of swellings. If not accounted for and corrected, these artifacts can change significantly the results from morphometric analysis and computer simulations. Here we present N3DFix, an open-source software which uses a correction algorithm to automatically find and fix swelling artifacts in neuronal reconstructions. N3DFix works as a post-processing tool and therefore can be used in either manual or (semi-)automatic reconstructions. The algorithm's internal parameters have been defined using a "ground truth" dataset produced by a neuroanatomist, involving two complementary manual reconstruction procedures: in the first, neuronal topology was faithfully reconstructed, including all swelling artifacts; in the second procedure a meticulous correction of the artifacts was manually performed directly during neuronal tracing. The internal parameters of N3DFix were set to minimize the differences between manual amendments and the algorithm's corrections. It is shown that the performance of N3DFix is comparable to careful manual correction of the swelling artifacts. To promote easy access and wide adoption, N3DFix is available in NEURON, Vaa3D and Py3DN.

Semi-automatic 3D morphological reconstruction of neurons with densely branching morphology: Application to retinal AII amacrine cells imaged with multi-photon excitation microscopy

BACKGROUND

Accurate reconstruction of the morphology of single neurons is important for morphometric studies and for developing compartmental models. However, manual morphological reconstruction can be extremely time-consuming and error-prone and algorithms for automatic reconstruction can be challenged when applied to neurons with a high density of extensively branching processes.

NEW METHOD

We present a procedure for semi-automatic reconstruction specifically adapted for densely branching neurons such as the AII amacrine cell found in mammalian retinas. We used whole-cell recording to fill AII amacrine cells in rat retinal slices with fluorescent dyes and acquired digital image stacks with multi-photon excitation microscopy. Our reconstruction algorithm combines elements of existing procedures, with segmentation based on adaptive thresholding and reconstruction based on a minimal spanning tree. We improved this workflow with an algorithm that reconnects neuron segments that are disconnected after adaptive thresholding, using paths extracted from the image stacks with the Fast Marching method.

RESULTS

By reducing the likelihood that disconnected segments were incorrectly connected to neighboring segments, our procedure generated excellent morphological reconstructions of AII amacrine cells.

COMPARISON WITH EXISTING METHODS

Reconstructing an AII amacrine cell required about 2h computing time, compared to 2-4days for manual reconstruction. To evaluate the performance of our method relative to manual reconstruction, we performed detailed analysis using a measure of tree structure similarity (DIADEM score), the degree of projection area overlap (Dice coefficient), and branch statistics.

CONCLUSIONS

We expect our procedure to be generally useful for morphological reconstruction of neurons filled with fluorescent dyes.