Biology subjects

Park, C. F.

Publications and source records attributed to Park, C. F..

2 recordsLinked to original sources

bioRxiv2023

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops

Connectomics is fundamental in propelling our understanding of the nervous systems organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from 4 different animals and 5 datasets, amounting to around 180 hours of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of 4 pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

neuroscience↗

bioRxiv2022

Automated neuron tracking inside moving and deforming animals using deep learning and targeted augmentation

Advances in functional brain imaging now allow sustained rapid 3D visualization of large numbers of neurons inside behaving animals. To decode circuit activity, imaged neurons must be individually segmented and tracked. This is particularly challenging when the brain itself moves and deforms inside a flexible body. The field has lacked general methods for solving this problem effectively. To address this need, we developed a method based on a convolutional neural network (CNN) Awith specific enhancements which we apply to freely moving Caenorhabditis elegans. For a traditional CNN to track neurons across images of a brain with different postures, the CNN must be trained with ground truth (GT) annotations of similar postures. When these postures are diverse, an adequate number of GT annotations can be prohibitively large to generate manually. We introduce targeted augmentation, a method to automatically synthesize reliable annotations from a few manual annotations. Our method effectively learns the internal deformations of the brain. The learned deformations are used to synthesize annotations for new postures by deforming the manual annotations of similar postures in GT images. The technique is germane to 3D images, which are generally more difficult to analyze than 2D images. The synthetic annotations, which are added to diversify training datasets, drastically reduce manual annotation and proofreading. Our method is effective both when neurons are represented as individual points or as 3D volumes. We provide a GUI that incorporates targeted augmentation in an end-to-end pipeline, from manual GT annotation of a few images to final proofreading of all images. We apply the method to simultaneously measure activity in the second-layer interneurons in C. elegans: RIA, RIB, and RIM, including the RIA neurite. We find that these neurons show rich behaviors, including switching entrainment on and off dynamically when the animal is exposed to periodic odor pulses.

neuroscience↗