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Jeff W. Lichtman

Jeff W. Lichtman appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Computer Vision cond-mat.mtrl-sci Artificial Intelligence eess.IV Neurons and Cognition physics.atom-ph physics.ins-det

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preprint2026arXiv

MicroscopyMatching: Towards a Ready-to-use Framework for Microscopy Image Analysis in Diverse Conditions

Analyzing microscopy images to extract biological object properties (e.g., their morphological organization, temporal dynamics, and population density) is fundamental to various biomedical research. Yet conducting this manually is costly and time-consuming. Though deep learning-based approaches have been explored to automate this process, the substantial diversity of microscopy analysis settings in practice (including variations of biological object types, sample processing protocols, imaging equipment, and analysis tasks, etc.) often renders them ineffective. As a result, these approaches typically require extensive adaptation for different settings, which, however, can impose burdens that are often practically unsustainable for laboratories, forcing biomedical researchers to still commonly rely on manual analysis, thereby severely bottlenecking the pace of biomedical research progress. This situation has created a pressing and long-standing need for a reliable and broadly applicable microscopy image analysis tool, yet such a tool is still missing. To address this gap, we present the first ready-to-use microscopy image analysis framework, MicroscopyMatching, that can reliably perform key analysis tasks (including segmentation, tracking, and counting) across diverse microscopy analysis settings. From a fundamentally different perspective, MicroscopyMatching reformulates diverse microscopy image analysis tasks as a unified matching problem, effectively handling this problem by exploiting the robust matching capability from pre-trained latent diffusion models.

preprint2021arXiv

Learning Guided Electron Microscopy with Active Acquisition

Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection. Datasets are typically acquired with uniform acquisition: applying the electron beam with the same power and duration to all image pixels, even if there is great variety in the pixels' importance for eventual use. Many SEMs are now able to move the beam to any pixel in the field of view without delay, enabling them, in principle, to invest their time budget more effectively with non-uniform imaging. In this paper, we show how to use deep learning to accelerate and optimize single-beam SEM acquisition of images. Our algorithm rapidly collects an information-lossy image (e.g. low resolution) and then applies a novel learning method to identify a small subset of pixels to be collected at higher resolution based on a trade-off between the saliency and spatial diversity. We demonstrate the efficacy of this novel technique for active acquisition by speeding up the task of collecting connectomic datasets for neurobiology by up to an order of magnitude.

preprint2020arXiv

A Topological Nomenclature for 3D Shape Analysis in Connectomics

One of the essential tasks in connectomics is the morphology analysis of neurons and organelles like mitochondria to shed light on their biological properties. However, these biological objects often have tangled parts or complex branching patterns, which make it hard to abstract, categorize, and manipulate their morphology. In this paper, we develop a novel topological nomenclature system to name these objects like the appellation for chemical compounds to promote neuroscience analysis based on their skeletal structures. We first convert the volumetric representation into the topology-preserving reduced graph to untangle the objects. Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation. In ablation studies, we quantitatively show that graphs generated by our proposed method align with the perception of experts. On 3D shape retrieval and decomposition tasks, we qualitatively demonstrate that the encoded topological nomenclature features achieve better results than state-of-the-art shape descriptors. To advance neuroscience, we will release a 3D segmentation dataset of mitochondria and pyramidal neurons reconstructed from a 100um cube electron microscopy volume with their reduced graph and topological nomenclature annotations. Code is publicly available at https://github.com/donglaiw/ibexHelper.

preprint2016arXiv

Efficiency of Cathodoluminescence Emission by Nitrogen-Vacancy Color Centers in Nanodiamond

Correlated electron microscopy and cathodoluminescence (CL) imaging using functionalized nanoparticles is a promising nanoscale probe of biological structure and function. Nanodiamonds (NDs) that contain CL-emitting color centers are particularly well suited for such applications. The intensity of CL emission from NDs is determined by a combination of factors, including: particle size; density of color centers; efficiency of energy deposition by electrons passing through the particle; and conversion efficiency from deposited energy to CL emission. We report experiments and numerical simulations that investigate the relative importance of each of these factors in determining CL emission intensity from NDs containing nitrogen-vacancy (NV) color centers. In particular, we find that CL can be detected from NV-doped NDs with dimensions as small as ~ 40 nm, although CL emission decreases significantly for smaller NDs.

preprint2016arXiv

RhoanaNet Pipeline: Dense Automatic Neural Annotation

Reconstructing a synaptic wiring diagram, or connectome, from electron microscopy (EM) images of brain tissue currently requires many hours of manual annotation or proofreading (Kasthuri and Lichtman, 2010; Lichtman and Sanes, 2008; Seung, 2009). The desire to reconstruct ever larger and more complex networks has pushed the collection of ever larger EM datasets. A cubic millimeter of raw imaging data would take up 1 PB of storage and present an annotation project that would be impractical without relying heavily on automatic segmentation methods. The RhoanaNet image processing pipeline was developed to automatically segment large volumes of EM data and ease the burden of manual proofreading and annotation. Based on (Kaynig et al., 2015), we updated every stage of the software pipeline to provide better throughput performance and higher quality segmentation results. We used state of the art deep learning techniques to generate improved membrane probability maps, and Gala (Nunez-Iglesias et al., 2014) was used to agglomerate 2D segments into 3D objects. We applied the RhoanaNet pipeline to four densely annotated EM datasets, two from mouse cortex, one from cerebellum and one from mouse lateral geniculate nucleus (LGN). All training and test data is made available for benchmark comparisons. The best segmentation results obtained gave $V^\text{Info}_\text{F-score}$ scores of 0.9054 and 09182 for the cortex datasets, 0.9438 for LGN, and 0.9150 for Cerebellum. The RhoanaNet pipeline is open source software. All source code, training data, test data, and annotations for all four benchmark datasets are available at www.rhoana.org.

preprint2014arXiv

Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes

In this paper, we present a new pipeline which automatically identifies and annotates axoplasmic reticula, which are small subcellular structures present only in axons. We run our algorithm on the Kasthuri11 dataset, which was color corrected using gradient-domain techniques to adjust contrast. We use a bilateral filter to smooth out the noise in this data while preserving edges, which highlights axoplasmic reticula. These axoplasmic reticula are then annotated using a morphological region growing algorithm. Additionally, we perform Laplacian sharpening on the bilaterally filtered data to enhance edges, and repeat the morphological region growing algorithm to annotate more axoplasmic reticula. We track our annotations through the slices to improve precision, and to create long objects to aid in segment merging. This method annotates axoplasmic reticula with high precision. Our algorithm can easily be adapted to annotate axoplasmic reticula in different sets of brain data by changing a few thresholds. The contribution of this work is the introduction of a straightforward and robust pipeline which annotates axoplasmic reticula with high precision, contributing towards advancements in automatic feature annotations in neural EM data.

preprint2014arXiv

Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes using High-Resolution Neural EM Data

Accurately estimating the wiring diagram of a brain, known as a connectome, at an ultrastructure level is an open research problem. Specifically, precisely tracking neural processes is difficult, especially across many image slices. Here, we propose a novel method to automatically identify and annotate small subcellular structures present in axons, known as axoplasmic reticula, through a 3D volume of high-resolution neural electron microscopy data. Our method produces high precision annotations, which can help improve automatic segmentation by using our results as seeds for segmentation, and as cues to aid segment merging.

preprint2013arXiv

Silicon-Vacancy Color Centers in Nanodiamonds: Cathodoluminescence Imaging Marker in the Near Infrared

We demonstrate that nanodiamonds fabricated to incorporate silicon-vacancy (Si-V) color centers provide bright, spectrally narrow, and stable cathodoluminescence (CL) in the near-infrared. Si-V color centers containing nanodiamonds are promising as non-bleaching optical markers for correlated CL and secondary electron microscopy, including applications to nanoscale bioimaging.

preprint2012arXiv

Multi-color correlative light and electron microscopy using nanoparticle cathodoluminescence

Correlative light and electron microscopy promises to combine molecular specificity with nanoscale imaging resolution. However, there are substantial technical challenges including reliable co-registration of optical and electron images, and rapid optical signal degradation under electron beam irradiation. Here, we introduce a new approach to solve these problems: multi-color imaging of stable optical cathodoluminescence emitted in a scanning electron microscope by nanoparticles with controllable surface chemistry. We demonstrate well-correlated cathodoluminescence and secondary electron images using three species of semiconductor nanoparticles that contain defects providing stable, spectrally-distinguishable cathodoluminescence. We also demonstrate reliable surface functionalization of the particles. The results pave the way for the use of such nanoparticles for targeted labeling of surfaces to provide nanoscale mapping of molecular composition, indicated by cathodoluminescence color, simultaneously acquired with structural electron images in a single instrument.