Gutierrez, C. E. | bioRxiv Science

bioRxiv2022

The Brain/MINDS Marmoset Connectivity Atlas: Exploring bidirectional tracing and tractography in the same stereotaxic space

We report on the implementation and features of the Brain/MINDS Marmoset Connectivity Atlas, BMCA, a new resource that provides access to anterograde neuronal tracer data in the prefrontal cortex of a marmoset brain. Neuronal tracers combined with fluorescence microscopy are a key technology for the systematic mapping of structural brain connectivity. We selected the prefrontal cortex for mapping due to its important role in higher brain functions. This work introduces the BMCA standard image preprocessing pipeline and tools for exploring and reviewing the data. We developed the BMCA-Explorer, which is an online image viewer designed for data exploration. Unlike other existing image explorers, it visualizes the data of different individuals in a common reference space at an unprecedented high resolution, facilitating comparative studies. To foster the integration with other marmoset brain image databases and cross-species comparisons, we added fiber tractography data from diffusion MRI, retrograde neural tracer data from the Marmoset Brain Connectivity Atlas project, and tools to map image data between marmoset and the human brain image space. This version of BMCA allows direct comparison between the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset.

neuroscience↗

bioRxiv2021

Automated segmentation of insect anatomy from micro-CT images using deep learning

Three-dimensional (3D) imaging, such as micro-computed tomography (micro-CT), is increasingly being used by organismal biologists for precise and comprehensive anatomical characterization. However, the segmentation of anatomical structures remains a bottleneck in research, often requiring tedious manual work. Here, we propose a pipeline for the fully-automated segmentation of anatomical structures in micro-CT images utilizing state-of-the-art deep learning methods, selecting the ant brain as a test case. We implemented the U-Net architecture for 2D image segmentation for our convolutional neural network (CNN), combined with pixel-island detection. For training and validation of the network, we assembled a dataset of semi-manually segmented brain images of 94 ant species. The trained network predicted the brain area in ant images fast and accurately; its performance tested on validation sets showed good agreement between the prediction and the target, scoring 80% Intersection over Union (IoU) and 90% Dice Coefficient (F1) accuracy. While manual segmentation usually takes many hours for each brain, the trained network takes only a few minutes. Furthermore, our network is generalizable for segmenting the whole neural system in full-body scans, and works in tests on distantly related and morphologically divergent insects (e.g., fruit flies). The latter suggest that methods like the one presented here generally apply across diverse taxa. Our method makes the construction of segmented maps and the morphological quantification of different species more efficient and scalable to large datasets, a step toward a big data approach to organismal anatomy.

evolutionary biology↗