AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphometry across species, or to validate novel non-invasive quantitative magnetic resonance imaging techniques. Most currently-available segmentation algorithms are based on standard image processing and usually require multiple processing steps and/or parameter tuning by the user to adapt to different modalities. Moreover, only few methods are publicly available. We introduce AxonDeepSeg, an open-source software that performs axon and myelin segmentation of microscopic images using deep learning. AxonDeepSeg features: (i) a convolutional neural network architecture; (ii) an easy training procedure to generate new models based on manually-labelled data and (iii) two ready-to-use models trained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results show high pixel-wise accuracy across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and 84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed and morphological metrics are extracted and compared against the literature. AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepsegComment: 14 pages, 7 figure

Computational vision applied to the segmentation and morphometric characterization of the sciatic nerve in microscopic images

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Automated 3D Axonal Morphometry of White Matter

Peer reviewe

Towards in vivo g-ratio mapping using MRI: unifying myelin and diffusion imaging

The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. This is the second review on the topic of g-ratio mapping using MRI. As such, it summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. Using simulations based on recently published data, this review demonstrates the relevance of the calibration step for three myelin-markers (macromolecular tissue volume, myelin water fraction, and bound pool fraction). It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of many novel techniques yet to be investigated.Comment: Will be published as a review article in Journal of Neuroscience Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest Editor

Flexible learning-free segmentation and reconstruction of neural volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively

Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variations

G-ratio is crucial for understanding the nervous system's health and function as it measures the relative myelin thickness around an axon. However, manual measurement is biased and variable, emphasizing the need for an automated and standardized technique. Although deep learning holds promise, current implementations lack clinical relevance and generalizability. This study aimed to develop an automated pipeline for selecting nerve fibers and calculating relevant g-ratio using quality parameters in optical microscopy. Histological sections from the sciatic nerves of 16 female mice were prepared and stained with either p-phenylenediamine (PPD) or toluidine blue (TB). A custom UNet model was trained on a mix of both types of staining to segment the sections based on 7,694 manually delineated nerve fibers. Post-processing excluded non-relevant nerves. Axon diameter, myelin thickness, and g-ratio were computed from the segmentation results and its reliability was assessed using the intraclass correlation coefficient (ICC). Validation was performed on adjacent cuts of the same nerve. Then, morphometrical analyses of both staining techniques were performed. High agreement with the ground truth was shown by the model, with dice scores of 0.86 (axon) and 0.80 (myelin) and pixel-wise accuracy of 0.98 (axon) and 0.94 (myelin). Good inter-device reliability was observed with ICC at 0.87 (g-ratio) and 0.83 (myelin thickness), and an excellent ICC of 0.99 for axon diameter. Although axon diameter significantly differed from the ground truth (p = 0.006), g-ratio (p = 0.098) and myelin thickness (p = 0.877) showed no significant differences. No statistical differences in morphological parameters (g-ratio, myelin thickness, and axon diameter) were found in adjacent cuts of the same nerve (ANOVA p-values: 0.34, 0.34, and 0.39, respectively). Comparing all animals, staining techniques yielded significant differences in mean g-ratio (PPD: 0.48 ± 0.04, TB: 0.50 ± 0.04), myelin thickness (PPD: 0.83 ± 0.28 μm, TB: 0.60 ± 0.20 μm), and axon diameter (PPD: 1.80 ± 0.63 μm, TB: 1.78 ± 0.63 μm). The proposed pipeline automatically selects relevant nerve fibers for g-ratio calculation in optical microscopy. This provides a reliable measurement method and serves as a potential pre-selection approach for large datasets in the context of healthy tissue. It remains to be demonstrated whether this method is applicable to measure g-ratio related with neurological disorders by comparing healthy and pathological tissue. Additionally, our findings emphasize the need for careful interpretation of inter-staining morphological parameters