Semi-Automatic Generation of Tight Binary Masks and Non-Convex
Isosurfaces for Quantitative Analysis of 3D Biological Samples
- URL: http://arxiv.org/abs/2001.11469v1
- Date: Thu, 30 Jan 2020 17:36:42 GMT
- Title: Semi-Automatic Generation of Tight Binary Masks and Non-Convex
Isosurfaces for Quantitative Analysis of 3D Biological Samples
- Authors: Sourabh Bhide, Ralf Mikut, Maria Leptin, Johannes Stegmaier
- Abstract summary: Current microscopy allows us (3D+t) of complete organisms (3D+t) to offer insights into their development on the cellular level.
Even though imaging speed and quality is steadily improving, fully- segmentation analysis methods are not accurate enough.
This is true while imaging (100um - 1mm) and deep inside the specimen.
We developed a system for analyzing quantitatively 3D+t light-sheet microscopy images of embryos.
- Score: 0.2711107673793059
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Current in vivo microscopy allows us detailed spatiotemporal imaging (3D+t)
of complete organisms and offers insights into their development on the
cellular level. Even though the imaging speed and quality is steadily
improving, fully-automated segmentation and analysis methods are often not
accurate enough. This is particularly true while imaging large samples (100um -
1mm) and deep inside the specimen. Drosophila embryogenesis, widely used as a
developmental paradigm, presents an example for such a challenge, especially
where cell outlines need to imaged - a general challenge in other systems as
well. To deal with the current bottleneck in analyzing quantitatively the 3D+t
light-sheet microscopy images of Drosophila embryos, we developed a collection
of semi-automatic open-source tools. The presented methods include a
semi-automatic masking procedure, automatic projection of non-convex 3D
isosurfaces to 2D representations as well as cell segmentation and tracking.
Related papers
- Super-resolution of biomedical volumes with 2D supervision [84.5255884646906]
Masked slice diffusion for super-resolution exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens.
We focus on the application of SliceR to stimulated histology (SRH), characterized by its rapid acquisition of high-resolution 2D images but slow and costly optical z-sectioning.
arXiv Detail & Related papers (2024-04-15T02:41:55Z) - GeoGS3D: Single-view 3D Reconstruction via Geometric-aware Diffusion Model and Gaussian Splatting [81.03553265684184]
We introduce GeoGS3D, a framework for reconstructing detailed 3D objects from single-view images.
We propose a novel metric, Gaussian Divergence Significance (GDS), to prune unnecessary operations during optimization.
Experiments demonstrate that GeoGS3D generates images with high consistency across views and reconstructs high-quality 3D objects.
arXiv Detail & Related papers (2024-03-15T12:24:36Z) - Nondestructive, quantitative viability analysis of 3D tissue cultures
using machine learning image segmentation [0.0]
We demonstrate an image processing algorithm for quantifying cellular viability in 3D cultures without the need for assay-based indicators.
We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions.
arXiv Detail & Related papers (2023-11-15T20:28:31Z) - Towards Model Generalization for Monocular 3D Object Detection [57.25828870799331]
We present an effective unified camera-generalized paradigm (CGP) for Mono3D object detection.
We also propose the 2D-3D geometry-consistent object scaling strategy (GCOS) to bridge the gap via an instance-level augment.
Our method called DGMono3D achieves remarkable performance on all evaluated datasets and surpasses the SoTA unsupervised domain adaptation scheme.
arXiv Detail & Related papers (2022-05-23T23:05:07Z) - Low dosage 3D volume fluorescence microscopy imaging using compressive
sensing [0.0]
We present a compressive sensing (CS) based approach to fully reconstruct 3D volumes with the same signal-to-noise ratio (SNR) with less than half of the excitation dosage.
We demonstrate our technique by capturing a 3D volume of the RFP labeled neurons in the zebrafish embryo spinal cord with the axial sampling of 0.1um using a confocal microscope.
The developed CS-based methodology in this work can be easily applied to other deep imaging modalities such as two-photon and light-sheet microscopy, where reducing sample photo-toxicity is a critical challenge.
arXiv Detail & Related papers (2022-01-03T18:44:50Z) - 3D Reconstruction of Curvilinear Structures with Stereo Matching
DeepConvolutional Neural Networks [52.710012864395246]
We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs.
We mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.
arXiv Detail & Related papers (2021-10-14T23:05:47Z) - Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo [103.08512487830669]
We present a modern solution to the multi-view photometric stereo problem (MVPS)
We procure the surface orientation using a photometric stereo (PS) image formation model and blend it with a multi-view neural radiance field representation to recover the object's surface geometry.
Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
arXiv Detail & Related papers (2021-10-11T20:20:03Z) - Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images
using Generative Adversarial Networks [0.11719282046304678]
Recent developments in fluorescence microscopy allow capturing high-resolution 3D images over time for living model organisms.
To be able to image even large specimens, techniques like multi-view light-sheet imaging record different orientations at each time point.
CNN-based multi-view deconvolution and fusion with two synthetic data sets mimic developing embryos and involve either two or four complementary 3D views.
arXiv Detail & Related papers (2021-08-05T17:21:01Z) - 3D fluorescence microscopy data synthesis for segmentation and
benchmarking [0.9922927990501083]
Conditional generative adversarial networks can be utilized to generate realistic image data for 3D fluorescence microscopy.
An additional positional conditioning of the cellular structures enables the reconstruction of position-dependent intensity characteristics.
A patch-wise working principle and a subsequent full-size reassemble strategy is used to generate image data of arbitrary size and different organisms.
arXiv Detail & Related papers (2021-07-21T16:08:56Z) - Towards an Automatic Analysis of CHO-K1 Suspension Growth in
Microfluidic Single-cell Cultivation [63.94623495501023]
We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data.
Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
arXiv Detail & Related papers (2020-10-20T08:36:51Z) - Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening [7.213084307941148]
We propose the first deep learning framework, XCryoNet, for automated cryo-EM grid screening.
XCryoNet is a semi-supervised, attention-guided deep learning approach that provides explainable scoring of automatically extracted square images.
Results show up to 8% and 37% improvements over a fully supervised and a no-attention solution, respectively, when labeled data is scarce.
arXiv Detail & Related papers (2020-07-10T20:11:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.