Miniscope3D: optimized single-shot miniature 3D fluorescence microscopy

• URL: http://arxiv.org/abs/2010.05382v1
• Date: Mon, 12 Oct 2020 01:19:31 GMT
• Title: Miniscope3D: optimized single-shot miniature 3D fluorescence microscopy
• Authors: Kyrollos Yanny, Nick Antipa, William Liberti, Sam Dehaeck, Kristina Monakhova, Fanglin Linda Liu, Konlin Shen, Ren Ng and Laura Waller
• Abstract summary: Miniature fluorescence microscopes capture only 2D information, and modifications that enable 3D capabilities increase the size and weight. Here, we achieve the 3D capability by replacing the tube lens of a conventional 2D Miniscope with an optimized multifocal phase mask at the objective's aperture stop. We demonstrate a prototype that is 17 mm tall and weighs 2.5 grams, achieving 2.76 $mu$m lateral, and 15 $mu$m axial resolution across most of the 900x700x390 $mu m3$ volume at 40 volumes per second.
• Score: 8.3011168382078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Miniature fluorescence microscopes are a standard tool in systems biology. However, widefield miniature microscopes capture only 2D information, and modifications that enable 3D capabilities increase the size and weight and have poor resolution outside a narrow depth range. Here, we achieve the 3D capability by replacing the tube lens of a conventional 2D Miniscope with an optimized multifocal phase mask at the objective's aperture stop. Placing the phase mask at the aperture stop significantly reduces the size of the device, and varying the focal lengths enables a uniform resolution across a wide depth range. The phase mask encodes the 3D fluorescence intensity into a single 2D measurement, and the 3D volume is recovered by solving a sparsity-constrained inverse problem. We provide methods for designing and fabricating the phase mask and an efficient forward model that accounts for the field-varying aberrations in miniature objectives. We demonstrate a prototype that is 17 mm tall and weighs 2.5 grams, achieving 2.76 $\mu$m lateral, and 15 $\mu$m axial resolution across most of the 900x700x390 $\mu m^3$ volume at 40 volumes per second. The performance is validated experimentally on resolution targets, dynamic biological samples, and mouse brain tissue. Compared with existing miniature single-shot volume-capture implementations, our system is smaller and lighter and achieves a more than 2x better lateral and axial resolution throughout a 10x larger usable depth range. Our microscope design provides single-shot 3D imaging for applications where a compact platform matters, such as volumetric neural imaging in freely moving animals and 3D motion studies of dynamic samples in incubators and lab-on-a-chip devices.

Related papers

• LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image [72.14973729674995]
  Current 3D perception methods, particularly small models, struggle with processing logical reasoning, question-answering, and handling open scenario categories. We propose solutions: Spatial-Enhanced Local Feature Mining for better spatial feature extraction, 3D Query Token-Derived Info Decoding for precise geometric regression, and Geometry Projection-Based 3D Reasoning for handling camera focal length variations.
  arXiv  Detail & Related papers  (2024-08-14T10:00:16Z)
• VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection [80.62052650370416]
  monocular 3D object detection holds significant importance across various applications, including autonomous driving and robotics. In this paper, we present VFMM3D, an innovative framework that leverages the capabilities of Vision Foundation Models (VFMs) to accurately transform single-view images into LiDAR point cloud representations.
  arXiv  Detail & Related papers  (2024-04-15T03:12:12Z)
• 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)
• Single-shot ToF sensing with sub-mm precision using conventional CMOS sensors [7.114925332582435]
  We present a novel single-shot interferometric ToF camera targeted for precise 3D measurements of dynamic objects. In contrast to conventional ToF cameras, our device uses only off-the-shelf CCD/CMOS detectors and works at their native chip resolution. We present 3D measurements of small (cm-sized) objects with > 2 Mp point cloud resolution and up to sub-mm depth precision.
  arXiv  Detail & Related papers  (2022-12-02T01:50:36Z)
• 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)
• SGM3D: Stereo Guided Monocular 3D Object Detection [62.11858392862551]
  We propose a stereo-guided monocular 3D object detection network, termed SGM3D. We exploit robust 3D features extracted from stereo images to enhance the features learned from the monocular image. Our method can be integrated into many other monocular approaches to boost performance without introducing any extra computational cost.
  arXiv  Detail & Related papers  (2021-12-03T13:57:14Z)
• Light-field microscopy with correlated beams for extended volumetric imaging at the diffraction limit [0.0]
  We propose and experimentally demonstrate a light-field microscopy architecture based on light intensity correlation. We demonstrate the effectiveness of our technique in refocusing three-dimensional test targets and biological samples out of the focused plane.
  arXiv  Detail & Related papers  (2021-10-02T13:54:11Z)
• Axial-to-lateral super-resolution for 3D fluorescence microscopy using unsupervised deep learning [19.515134844947717]
  We present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in fluorescence microscopy. Our method greatly reduces the effort to put into practice as the training of a network requires as little as a single 3D image stack. We demonstrate that the trained network not only enhances axial resolution beyond the diffraction limit, but also enhances suppressed visual details between the imaging planes and removes imaging artifacts.
  arXiv  Detail & Related papers  (2021-04-19T16:31:12Z)
• Recurrent neural network-based volumetric fluorescence microscopy [0.30586855806896046]
  We report a deep learning-based image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope. Through a recurrent convolutional neural network, which we term as Recurrent-MZ, 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume. Recurrent-MZ is demonstrated to increase the depth-of-field of a 63xNA objective lens by approximately 50-fold, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume.
  arXiv  Detail & Related papers  (2020-10-21T06:17:38Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)
• Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials [65.95928593628128]
  We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo technique. Our algorithm is suitable for perspective cameras and nearby point light sources.
  arXiv  Detail & Related papers  (2020-01-18T12:26:22Z)

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.