3D Deep Convolutional Neural Networks in Lattice Light-Sheet Data Puncta Segmentation

Schöneberg, Johannes; Raghupathi, Gautham; Betzig, Eric; Drubin, David G.

doi:10.1109/bibm47256.2019.8983012

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Besides data visualization, further downstream data processing/analysis can be performed on the preprocessed 4D dataset (Figure 5C) . In the case of 4D particle tracking, pyLattice ParticleTracking 26 can be applied for particle segmentation and tracking. In the case of 4D analyses of mitochondrial motility and dynamics, pyLattice MitoTNT 27 can be used to reliably track and extract functional information from mitochondrial networks.…”

Section: Resultsmentioning

confidence: 99%

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm

Wang,

Hakozaki,

McMahon

et al. 2024

Preprint

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Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), rely on a tilting of the sample plane with respect to the light sheet of 30-45 degrees to ease sample preparation. Data from such tilted-sample-plane LSFMs require subsequent deskewing and rotation for proper visualization and analysis. Such transformations currently demand substantial memory allocation. This poses computational challenges, especially with large datasets. The consequence is long processing times compared to data acquisition times, which currently limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a novel GPU-accelerated memory-efficient algorithm that integrates deskewing and rotation into a single transformation, significantly reducing memory requirements and reducing the preprocessing run time by at least 10-fold for large image stacks. Benchmarked against conventional methods and existing software, our approach demonstrates linear scalability. Processing large 3D stacks of up to 15 GB is now possible within one minute using a single GPU with 24 GB of memory. Applied to 4D LLSM datasets of human hepatocytes, human lung organoid tissue, and human brain organoid tissue, our method outperforms alternatives, providing rapid, accurate preprocessing within seconds. Importantly, such processing speeds now allow visualization of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data processing, visualization, and analysis on standard workstations, thereby revolutionizing biological imaging applications for LLSM, SPIM and similar light microscopes.

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Section: Resultsmentioning

confidence: 99%

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm

Wang,

Hakozaki,

McMahon

et al. 2024

Preprint

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“…Reliability of the tracking can be evaluated with the help of the provided tracking visualization module. Future efforts might use advances in machine learning [36,37] to improve segmentation reliability.…”

Section: Discussionmentioning

confidence: 99%

MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data

et al. 2023

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Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility. Dysregulation of this four-dimensional (4D: x,y,z,time) temporal network is implicated in numerous diseases ranging from cancer to neurodegeneration. While lattice light-sheet microscopy has recently made it possible to image mitochondria in 4D, quantitative analysis methods for the resulting datasets have been lacking. Here we present MitoTNT, the first-in-class software for Mitochondrial Temporal Network Tracking in 4D live-cell fluorescence microscopy data. MitoTNT uses spatial proximity and network topology to compute an optimal tracking assignment. To validate the accuracy of tracking, we created a reaction-diffusion simulation to model mitochondrial network motion and remodeling events. We found that our tracking is >90% accurate for the ground-truth simulations and agrees well with published motility results for experimental data. We used MitoTNT to quantify 4D mitochondrial networks from human induced pluripotent stem cells. First, we characterized sub-fragment motility and analyzed network branch motion patterns. We revealed that the skeleton node motion is correlated along branch and uncorrelated in time. Second, we identified fission and fusion events with high spatiotemporal resolution. We found that mitochondrial skeleton nodes near the fission/fusion sites move nearly twice as fast as random skeleton nodes and that microtubules play a role in mediating selective fission/fusion. Finally, we developed graph-based transport simulations that model how material would distribute on experimentally measured mitochondrial temporal networks. We showed that pharmacological perturbations increase network reachability but decrease network resilience through a combination of altered mitochondrial fission/fusion dynamics and motility. MitoTNT’s easy-to-use tracking module, interactive 4D visualization capability, and powerful post-tracking analysis aim at making temporal network tracking accessible to the wider mitochondria research community.

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“…Based on fluorescence microscopy and computational image segmentation, MitoTNT is limited by a microscope's ability to record high signal to noise volumetric images of the mitochondrial network to ensure high quality segmentation. Future efforts might use advances in machine learning [33][34][35] to improve segmentation quality and reliability. We highlighted three applications of MitoTNT: 1) high-resolution mitochondria network motility analysis, 2) node-level mitochondrial fission/fusion analysis, and 3) mitochondria temporal network analysis.…”

Section: Discussionmentioning

confidence: 99%

MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data

Wang

Natekar

Tea

et al. 2022

Preprint

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Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility. This four-dimensional (4D, x,y,z,time) temporal network has only recently been made accessible through advanced imaging methods such as lattice light-sheet microscopy. Quantitative analysis tools for the resulting datasets however have been lacking. Here we present MitoTNT, the first-in-class software for Mitochondrial Temporal Network Tracking in 4D live-cell fluorescence microscopy data. MitoTNT uses spatial proximity and network topology to compute an optimal tracking. Tracking is >90% accurate in dynamic spatial mitochondria simulations and are in agreement with published motility results in vitro. Using MitoTNT, we reveal correlated mitochondrial movement patterns, local fission and fusion fingerprints, asymmetric fission and fusion dynamics, cross-network transport patterns, and network-level responses to pharmacological manipulations. MitoTNT is implemented in python with a JupyterLab interface. The extendable and user-friendly design aims at making temporal network tracking accessible to the wider mitochondria community.

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3D Deep Convolutional Neural Networks in Lattice Light-Sheet Data Puncta Segmentation

Cited by 5 publications

References 15 publications

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm

MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data

MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data

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