6 research outputs found
Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction
We present a method combining affinity prediction with region agglomeration,
which improves significantly upon the state of the art of neuron segmentation
from electron microscopy (EM) in accuracy and scalability. Our method consists
of a 3D U-NET, trained to predict affinities between voxels, followed by
iterative region agglomeration. We train using a structured loss based on
MALIS, encouraging topologically correct segmentations obtained from affinity
thresholding. Our extension consists of two parts: First, we present a
quasi-linear method to compute the loss gradient, improving over the original
quadratic algorithm. Second, we compute the gradient in two separate passes to
avoid spurious gradient contributions in early training stages. Our predictions
are accurate enough that simple learning-free percentile-based agglomeration
outperforms more involved methods used earlier on inferior predictions. We
present results on three diverse EM datasets, achieving relative improvements
over previous results of 27%, 15%, and 250%. Our findings suggest that a single
method can be applied to both nearly isotropic block-face EM data and
anisotropic serial sectioned EM data. The runtime of our method scales linearly
with the size of the volume and achieves a throughput of about 2.6 seconds per
megavoxel, qualifying our method for the processing of very large datasets
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Local shape descriptors for neuron segmentation
We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets
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Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging.
The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching and phototoxicity. Here we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo and Depth-Aware Video Frame Interpolation, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series post-acquisition. We show that CAFI is capable of understanding the motion context of biological structures and can perform better than standard interpolation methods. We benchmark CAFIs performance on 12 different datasets, obtained from four different microscopy modalities, and demonstrate its capabilities for single-particle tracking and nuclear segmentation. CAFI potentially allows for reduced light exposure and phototoxicity on the sample for improved long-term live-cell imaging. The models and the training and testing data are available via the ZeroCostDL4Mic platform
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Induction of lysosomal and mitochondrial biogenesis by AMPK phosphorylation of FNIP1.
Cells respond to mitochondrial poisons with rapid activation of the adenosine monophosphate-activated protein kinase (AMPK), causing acute metabolic changes through phosphorylation and prolonged adaptation of metabolism through transcriptional effects. Transcription factor EB (TFEB) is a major effector of AMPK that increases expression of lysosome genes in response to energetic stress, but how AMPK activates TFEB remains unresolved. We demonstrate that AMPK directly phosphorylates five conserved serine residues in folliculin-interacting protein 1 (FNIP1), suppressing the function of the folliculin (FLCN)-FNIP1 complex. FNIP1 phosphorylation is required for AMPK to induce nuclear translocation of TFEB and TFEB-dependent increases of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α) and estrogen-related receptor alpha (ERRα) messenger RNAs. Thus, mitochondrial damage triggers AMPK-FNIP1-dependent nuclear translocation of TFEB, inducing sequential waves of lysosomal and mitochondrial biogenesis