Software tools for writing image analysis code

I was recently at a small meeting at UC Berkeley to get together engineers, computer scientists, and biologists around the theme of computational imaging, and more generally to get the various groups at UCB who are working on similar problems talking to each other. Aside from hearing about a lot of interesting research being done, I learned about some work being done to make programming languages specifically for image analysis. The goal here is to decouple knowledge of the algorithms to solve the image analysis problem from the problem to be solved, so that the people who are not experts in computation can write image analysis code that is fast.

I haven’t tried either of these tools yet, but they both look interesting. One is an embedded language for Python called ProxImaL that formulates operations like deblurring and denoising as constrained optimizations. The other is a C++ embedded language called Halide designed to make it easy to write high performance image analysis code that can be compiled to multiple targets (CPU, GPU, etc.) .

Both of these are a little beyond my current programming experience but they sound like tools that should be more widely known.

Paper Roundup – August 2016

  • A new far-red fluorescent protein that uses biliverdin as a chromophore and is brighter than existing far red FPs [1]
  • A review of quantum dot blinking and how to control it [2]
  • Photoactivatible versions of the Janelia Farms (JF) dyes for single molecule imaging [3]
  • Comparison of different clearing methods for mouse embryos and hearts [4]
  • A malachite green fluorogen-activating protein that outperforms Cy5 for single molecule imaging [5]
  • A general model for counting molecules in single-molecule microscopy [6]
  • Stimulated Raman scattering imaging of bioorthogonal probes [7]
  • Multiview image capture and fusion for resolution improvement in widefield and light sheet microscopy [8]
  • Combining photoswitching and analytical ultracentrifugation to interrogate complex binding equilibria [9]
  • A simplified CLARITY clearing method, eliminating the need for removal of oxygen prior to polymerization [10]
  • A custom two-photon microscope for wide field-of-view imaging [11]
  • Pulsed illumination reduces phototoxicity and photobleaching [12]
  • Identifying clusters in localization microscopy images by varying labeling density [13]
  • Reversible cryo-arrest of cells by chilling to -45°C on a microscope [14]
  • Correlation between hybridizations to measure transcript number by imaging [15]
  • Using a speckle scrambler to improve illumination uniformity in TIRF and localization microscopy [16]
  • Monomeric near-infrared fluorescent proteins [17]

References

  1. E.A. Rodriguez, G.N. Tran, L.A. Gross, J.L. Crisp, X. Shu, J.Y. Lin, and R.Y. Tsien, "A far-red fluorescent protein evolved from a cyanobacterial phycobiliprotein", Nature Methods, vol. 13, pp. 763-769, 2016. http://dx.doi.org/10.1038/nmeth.3935
  2. A.L. Efros, and D.J. Nesbitt, "Origin and control of blinking in quantum dots", Nature Nanotechnology, vol. 11, pp. 661-671, 2016. http://dx.doi.org/10.1038/nnano.2016.140
  3. L.D. Lavis, J.B. Grimm, B.P. English, A.K. Muthusamy, B.P. Mehl, P. Dong, T.A. Brown, Z. Liu, and T. Lionnet, "Bright photoactivatable fluorophores for single-molecule imaging", 2016. http://dx.doi.org/10.1101/066779
  4. H. Kolesová, M. Čapek, B. Radochová, J. Janáček, and D. Sedmera, "Comparison of different tissue clearing methods and 3D imaging techniques for visualization of GFP-expressing mouse embryos and embryonic hearts", Histochemistry and Cell Biology, vol. 146, pp. 141-152, 2016. http://dx.doi.org/10.1007/s00418-016-1441-8
  5. S. Saurabh, A.M. Perez, C.J. Comerci, L. Shapiro, and W.E. Moerner, "Super-resolution Imaging of Live Bacteria Cells Using a Genetically Directed, Highly Photostable Fluoromodule", J. Am. Chem. Soc., vol. 138, pp. 10398-10401, 2016. http://dx.doi.org/10.1021/jacs.6b05943
  6. G. Hummer, F. Fricke, and M. Heilemann, "Model-independent counting of molecules in single-molecule localization microscopy", Molecular Biology of the Cell, 2016. http://dx.doi.org/10.1091/mbc.E16-07-0525
  7. L. Wei, F. Hu, Z. Chen, Y. Shen, L. Zhang, and W. Min, "Live-Cell Bioorthogonal Chemical Imaging: Stimulated Raman Scattering Microscopy of Vibrational Probes", Accounts of Chemical Research, vol. 49, pp. 1494-1502, 2016. http://dx.doi.org/10.1021/acs.accounts.6b00210
  8. Y. Wu, P. Chandris, P.W. Winter, E.Y. Kim, V. Jaumouillé, A. Kumar, M. Guo, J.M. Leung, C. Smith, I. Rey-Suarez, H. Liu, C.M. Waterman, K.S. Ramamurthi, P.J. La Riviere, and H. Shroff, "Simultaneous multiview capture and fusion improves spatial resolution in wide-field and light-sheet microscopy", Optica, vol. 3, pp. 897, 2016. http://dx.doi.org/10.1364/OPTICA.3.000897
  9. H. Zhao, Y. Fu, C. Glasser, E.J. Andrade Alba, M.L. Mayer, G. Patterson, and P. Schuck, "Monochromatic multicomponent fluorescence sedimentation velocity for the study of high-affinity protein interactions", eLife, vol. 5, 2016. http://dx.doi.org/10.7554/eLife.17812
  10. K. Sung, Y. Ding, J. Ma, H. Chen, V. Huang, M. Cheng, C.F. Yang, J.T. Kim, D. Eguchi, D. Di Carlo, T.K. Hsiai, A. Nakano, and R.P. Kulkarni, "Simplified three-dimensional tissue clearing and incorporation of colorimetric phenotyping", Scientific Reports, vol. 6, pp. 30736, 2016. http://dx.doi.org/10.1038/srep30736
  11. J.N. Stirman, I.T. Smith, M.W. Kudenov, and S.L. Smith, "Wide field-of-view, multi-region, two-photon imaging of neuronal activity in the mammalian brain", Nature Biotechnology, vol. 34, pp. 857-862, 2016. http://dx.doi.org/10.1038/nbt.3594
  12. C. Boudreau, T.(. Wee, Y.(. Duh, M.P. Couto, K.H. Ardakani, and C.M. Brown, "Excitation Light Dose Engineering to Reduce Photo-bleaching and Photo-toxicity", Scientific Reports, vol. 6, pp. 30892, 2016. http://dx.doi.org/10.1038/srep30892
  13. F. Baumgart, A.M. Arnold, K. Leskovar, K. Staszek, M. Fölser, J. Weghuber, H. Stockinger, and G.J. Schütz, "Varying label density allows artifact-free analysis of membrane-protein nanoclusters", Nature Methods, vol. 13, pp. 661-664, 2016. http://dx.doi.org/10.1038/nmeth.3897
  14. M.E. Masip, J. Huebinger, J. Christmann, O. Sabet, F. Wehner, A. Konitsiotis, G.R. Fuhr, and P.I.H. Bastiaens, "Reversible cryo-arrest for imaging molecules in living cells at high spatial resolution", Nature Methods, vol. 13, pp. 665-672, 2016. http://dx.doi.org/10.1038/nmeth.3921
  15. A.F. Coskun, and L. Cai, "Dense transcript profiling in single cells by image correlation decoding", Nature Methods, vol. 13, pp. 657-660, 2016. http://dx.doi.org/10.1038/nmeth.3895
  16. P. GEORGIADES, V.J. ALLAN, M. DICKINSON, and T.A. WAIGH, "Reduction of coherent artefacts in super-resolution fluorescence localisation microscopy", Journal of Microscopy, 2016. http://dx.doi.org/10.1111/jmi.12453
  17. D.M. Shcherbakova, M. Baloban, A.V. Emelyanov, M. Brenowitz, P. Guo, and V.V. Verkhusha, "Bright monomeric near-infrared fluorescent proteins as tags and biosensors for multiscale imaging", Nature Communications, vol. 7, pp. 12405, 2016. http://dx.doi.org/10.1038/ncomms12405

High Speed PCIe SSDs

Those of you who’ve been reading this blog since it’s inception will remember that I used to post a lot about solid state drives, because we spent a lot of time trying to handle the 1 GB/sec bandwidth of sCMOS cameras back in 2013. We standardized on RAID 0 arrays of four Samsung Pro SSDs, and I stopped thinking about it, because that was good enough for our purposes.

Since then, however, quite a bit has changed. You can now get an 512 GB SSD card that can write at 1.5 GB/sec and read at 3 GB/sec (the Samsung Pro 950) for $350. Newer Samsung products promise slightly faster read / write speeds at disk sizes up to 1 TB. These use the M.2 interface, designed for SSDs, but PCIe to M.2 adapters are readily available if your motherboard doesn’t have an M.2 slot. To get full speeds you’ll need a motherboard with a PCIe 3.0  x4 slot available.

Another thing that’s changed is that sCMOS cameras have made it really easy to capture large data sets. In the last year people really seen to have taken advantage of this and we’re seeing a lot of people acquiring 100GB+ data sets, and often up to 1 TB. A big consequence of this is that data processing is increasingly becoming I/O bound. A mechanical hard drive tops out at around 120 MB/sec sequential read/write speed. At those speeds, just reading a 20 GB file takes around 3 min. We’ve seen this become a major issue where exporting a 500 GB data set from Nikon ND2 to TIFF takes hours.

If you’re doing any processing of large data sets, it’s very advantageous to have a fast drive like this to speed up I/O. There’s still a challenge in getting data on and off of these drives, since gigabit ethernet tops out at around 100 MB/sec, and most USB3 drives max out around 250 MB/sec (as an aside, the Samsung T3 looks pretty promising, with 450 MB/sec transfer rates). Finally, most people (at least here) still want their data on a mechanical drive for long term storage. But at least once you have data on the fast drive the I/O bottleneck gets better by about 10 – 20-fold.

All of this would be well worth thinking about if you’re building the compute environment for a microscopy core from scratch. I imagine you’d want fast local storage for data processing, 10 Gbit networking to move data around, and some kind of slow archiving to long term storage. You’d also want to educate people about data handling so that they think about these issues when designing their experiments.

Confocal imaging of a tick

I recently came back from the east coast with an unwanted guest attached to me: a tick, probably a lone star tick, Amblyomma americanum. After removing it, I decided to have some fun with it – I dehydrated it in methanol, cleared it in methyl salicylate, and then imaged it on our spinning disk confocal. The movie below is stitched from four images, and is about 1.8 mm on a side and 1.2 mm thick. Total image size is ~2800 x 2800 x 306. The fluorescence is endogenous autofluorescence excited at 488 and 561 nm. This is probably a nymphal tick, and it looks like the mouthparts are missing.

Paper Roundup – July 2016

  • A review of Raman imaging [1]
  • DNA-PAINT imaging of DNA origami with nanometer resolution [2]
  • A self-assembling icosahedron that can be used as a fluorescence standard in microscopy [3]
  • A split horseradish peroxidase for labelling of protein interactions [4]
  • A 4Pi single molecule switching microscope, with isotropic 20 nm resolution imaging [5]
  • A comparison of fluorescent protein performance in C. elegans [6]
  • Expansion microscopy with RNA FISH [7] and with conventional fluorescent proteins and antibodies [8]
  • A custom system for calcium imaging of freely walking flies [9]
  • Cell tracking software tools [10]
  • An in-incubator Fourier ptychography system for rapid imaging of 6-well plate [11]

References

  1. M. Cicerone, "Molecular imaging with CARS micro-spectroscopy", Current Opinion in Chemical Biology, vol. 33, pp. 179-185, 2016. http://dx.doi.org/10.1016/j.cbpa.2016.05.010
  2. M. Dai, R. Jungmann, and P. Yin, "Optical imaging of individual biomolecules in densely packed clusters", Nature Nanotechnology, vol. 11, pp. 798-807, 2016. http://dx.doi.org/10.1038/nnano.2016.95
  3. Y. Hsia, J.B. Bale, S. Gonen, D. Shi, W. Sheffler, K.K. Fong, U. Nattermann, C. Xu, P. Huang, R. Ravichandran, S. Yi, T.N. Davis, T. Gonen, N.P. King, and D. Baker, "Design of a hyperstable 60-subunit protein icosahedron", Nature, vol. 535, pp. 136-139, 2016. http://dx.doi.org/10.1038/nature18010
  4. J.D. Martell, M. Yamagata, T.J. Deerinck, S. Phan, C.G. Kwa, M.H. Ellisman, J.R. Sanes, and A.Y. Ting, "A split horseradish peroxidase for the detection of intercellular protein–protein interactions and sensitive visualization of synapses", Nature Biotechnology, vol. 34, pp. 774-780, 2016. http://dx.doi.org/10.1038/nbt.3563
  5. F. Huang, G. Sirinakis, E. Allgeyer, L. Schroeder, W. Duim, E. Kromann, T. Phan, F. Rivera-Molina, J. Myers, I. Irnov, M. Lessard, Y. Zhang, M. Handel, C. Jacobs-Wagner, C. Lusk, J. Rothman, D. Toomre, M. Booth, and J. Bewersdorf, "Ultra-High Resolution 3D Imaging of Whole Cells", Cell, vol. 166, pp. 1028-1040, 2016. http://dx.doi.org/10.1016/j.cell.2016.06.016
  6. J.K. Heppert, D.J. Dickinson, A.M. Pani, C.D. Higgins, A. Steward, J. Ahringer, J.R. Kuhn, and B. Goldstein, "Comparative assessment of fluorescent proteins for in vivo imaging in an animal model system", Molecular Biology of the Cell, 2016. http://dx.doi.org/10.1091/mbc.E16-01-0063
  7. F. Chen, A.T. Wassie, A.J. Cote, A. Sinha, S. Alon, S. Asano, E.R. Daugharthy, J. Chang, A. Marblestone, G.M. Church, A. Raj, and E.S. Boyden, "Nanoscale imaging of RNA with expansion microscopy", Nature Methods, vol. 13, pp. 679-684, 2016. http://dx.doi.org/10.1038/nMeth.3899
  8. P.W. Tillberg, F. Chen, K.D. Piatkevich, Y. Zhao, C.(. Yu, B.P. English, L. Gao, A. Martorell, H. Suk, F. Yoshida, E.M. DeGennaro, D.H. Roossien, G. Gong, U. Seneviratne, S.R. Tannenbaum, R. Desimone, D. Cai, and E.S. Boyden, "Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies", Nature Biotechnology, vol. 34, pp. 987-992, 2016. http://dx.doi.org/10.1038/nbt.3625
  9. D. Grover, T. Katsuki, and R.J. Greenspan, "Flyception: imaging brain activity in freely walking fruit flies", Nature Methods, vol. 13, pp. 569-572, 2016. http://dx.doi.org/10.1038/nmeth.3866
  10. O. Hilsenbeck, M. Schwarzfischer, S. Skylaki, B. Schauberger, P.S. Hoppe, D. Loeffler, K.D. Kokkaliaris, S. Hastreiter, E. Skylaki, A. Filipczyk, M. Strasser, F. Buggenthin, J.S. Feigelman, J. Krumsiek, A.J.J. van den Berg, M. Endele, M. Etzrodt, C. Marr, F.J. Theis, and T. Schroeder, "Software tools for single-cell tracking and quantification of cellular and molecular properties", Nature Biotechnology, vol. 34, pp. 703-706, 2016. http://dx.doi.org/10.1038/nbt.3626
  11. J. Kim, B.M. Henley, C.H. Kim, H.A. Lester, and C. Yang, "Incubator embedded cell culture imaging system (EmSight) based on Fourier ptychographic microscopy", Biomedical Optics Express, vol. 7, pp. 3097, 2016. http://dx.doi.org/10.1364/BOE.7.003097

Testing the Prime95B – a back-illuminated sCMOS camera with 95% QE

A few weeks ago I mentioned that Photometrics had released a new camera, the Prime95B, featuring a back-illuminated sCMOS sensor with 95% peak QE. I got a chance to play with it last week, and I’m pleased to say that it performs as well as you would expect. We compared it to an iXon 888 EMCCD mounted on our CSU-W1 spinning disk confocal. We had purchased this EMCCD for imaging those samples that were too dim to get good images with the Zyla 4.2 sCMOS camera we also have mounted on the confocal. (You can see a sketch of how everything is configured in this previous post). For testing the Prime95B, we replaced the Zyla 4.2 with the Prime95B, allowing us to directly compare the Prime95B and the iXon 888.

Before I get to the data, however, what performance do we expect? To get a sense of what to expect I wrote a Matlab script that calculates the theoretical performance for a number of different cameras, using their quantum efficiency, read noise, and excess noise factor (for EMCCDs). You can get the script here.   You can read more about how to calculate the signal-to-nosie ratio for a camera in this Hamamatsu white paper. Here, I’m ignoring the different pixel sizes of the various cameras by assuming that they all receive the same photon flux per pixel, as if the magnification had been adjusted to produce the same effective pixel size at the sample.

Theoretical performance of different cameras.

Theoretical performance of different cameras. Ideal is a theoretical ideal camera with QE=1 and no read noise. EMCCD assumes a high EM gain, ~200x; 82% QE sCMOS is a Flash4.0v2 or Zyla 4.2 Plus; 72% QE sCMOS is a Flash 4.0 or Zyla 4.2; ICX285 is a Coolsnap HQ2 or similar interline CCD camera.

Continue reading

USB3 Flash drives for high speed data transfer

In the past few years, we’ve switched the NIC almost exclusively to sCMOS cameras. We still have a few EMCCDs, but we have no interline CCDs in the NIC anymore. This change has greatly increased data acquisition rates – we’ve gone from 1.4 megapixel images to 4+ megapixel images. It’s now very common for someone to sit down at a microscope for a few hours and end up with 20 GB of data, and time lapse experiments often produce 1 TB.

This increase in the amount of data has made data transfer a bottleneck. USB2.0 flash drives, in our experience, top out around 20-30 MB/sec transfer rates. At that speed, a 20 GB data set takes 10-15 minutes to transfer. To reduce this time, we’ve begun to upgrade all of our PCs to add USB3 ports. We’ve been using Startech cards and have had good luck with them. With a good USB3 thumb drive – we use a SanDisk Extreme for testing – we can get transfer speeds of ~180 MB/sec, a 6-8x improvement in speed. In the process of doing this I’ve learned a few useful things. First, USB2 cables do not support USB3 transfer speeds – you need to have a USB3 cable. Second, not all USB3 hubs are equal – we have one that doesn’t manage USB3 transfer speeds. We’ve had good luck with this Anker USB3 hub. Finally, some devices (in particular the Nikon Ti) do not like to run over USB3 ports, so you still need some USB2.0 ports for controlling hardware.

We also have a network server for data transfer, but with gigabit ethernet, it maxes out at about 100 MB/sec transfer speeds, so the USB3 drives end up being somewhat faster. We’re hoping that the USB drives will be reliable enough to allow direct acquisition of data to them (rather than saving to the local hard drive and then copying), although we haven’t tried that yet. We have had problems with transfer glitches causing experiments to be interrupted when we save over the network or to USB2.0 drives, so we don’t recommend that.

There is now a USB3.1 Gen 2 specification that promises a 2x speed improvement over USB3.0, but very few drives support it, so we haven’t started looking at that.

Paper Roundup – June 2016

  • Quantitative comparison of fluorescent proteins. A great resource with measurements of photobleaching, brightness, and monomericness for a large number of fluorescent proteins [1]
  • Metalenses for focusing visible light with an NA of 0.8 [2]
  • A cyan-excitable orange-emitting fluorescent protein [3]
  • A mutant of UnaG that is nonfluorescent and destabilizing in the absence of ligand, and fluorescent and stable in the presence of ligand [4]
  • Spectral imaging for single particle tracking of motor proteins [5]
  • A fluorescent reporter for beta-galactosidase that can be used for cell and animal imaging [6]
  • All-optical electrophysiology [7]
  • A review of small molecule dyes for super-resolution imaging [8]
  • An improved protocol for expansion microscopy [9]
  • A review of clearing techniques [10]
  • A generative model for testing spatial distributions of puncta within the cell [11]
  • Mammalian protein tagging with CRISPR/Cas9 and split GFP for simple, scalable tagging of endogenous mammalian proteins [12]
  • Miniature light sheet generator modules [13]
  • An electrically-tunable lens to move the waist of a light sheet synchronously with the virtual detection slit on a sCMOS camera to make narrow light sheets over large areas [14]
  • A review of cyanine photobleaching mechanisms and their applications [15]

References

  1. P.J. Cranfill, B.R. Sell, M.A. Baird, J.R. Allen, Z. Lavagnino, H.M. de Gruiter, G. Kremers, M.W. Davidson, A. Ustione, and D.W. Piston, "Quantitative assessment of fluorescent proteins", Nature Methods, vol. 13, pp. 557-562, 2016. http://dx.doi.org/10.1038/nmeth.3891
  2. M. Khorasaninejad, W.T. Chen, R.C. Devlin, J. Oh, A.Y. Zhu, and F. Capasso, "Metalenses at visible wavelengths: Diffraction-limited focusing and subwavelength resolution imaging", Science, vol. 352, pp. 1190-1194, 2016. http://dx.doi.org/10.1126/science.aaf6644
  3. J. Chu, Y. Oh, A. Sens, N. Ataie, H. Dana, J.J. Macklin, T. Laviv, E.S. Welf, K.M. Dean, F. Zhang, B.B. Kim, C.T. Tang, M. Hu, M.A. Baird, M.W. Davidson, M.A. Kay, R. Fiolka, R. Yasuda, D.S. Kim, H. Ng, and M.Z. Lin, "A bright cyan-excitable orange fluorescent protein facilitates dual-emission microscopy and enhances bioluminescence imaging in vivo", Nat Biotechnol, vol. 34, pp. 760-767, 2016. http://dx.doi.org/10.1038/nbt.3550
  4. R. Navarro, L. Chen, R. Rakhit, and T.J. Wandless, "A Novel Destabilizing Domain Based on a Small-Molecule Dependent Fluorophore", ACS Chem. Biol., vol. 11, pp. 2101-2104, 2016. http://dx.doi.org/10.1021/acschembio.6b00234
  5. T. Kakizuka, K. Ikezaki, J. Kaneshiro, H. Fujita, T.M. Watanabe, and T. Ichimura, "Simultaneous nano-tracking of multiple motor proteins via spectral discrimination of quantum dots", Biomedical Optics Express, vol. 7, pp. 2475, 2016. http://dx.doi.org/10.1364/BOE.7.002475
  6. K. Gu, Y. Xu, H. Li, Z. Guo, S. Zhu, S. Zhu, P. Shi, T.D. James, H. Tian, and W. Zhu, "Real-Time Tracking and In Vivo Visualization of β-Galactosidase Activity in Colorectal Tumor with a Ratiometric Near-Infrared Fluorescent Probe", J. Am. Chem. Soc., vol. 138, pp. 5334-5340, 2016. http://dx.doi.org/10.1021/jacs.6b01705
  7. H. Zhang, E. Reichert, and A.E. Cohen, "Optical electrophysiology for probing function and pharmacology of voltage-gated ion channels", eLife, vol. 5, 2016. http://dx.doi.org/10.7554/eLife.15202
  8. Z. Yang, A. Sharma, J. Qi, X. Peng, D.Y. Lee, R. Hu, D. Lin, J. Qu, and J.S. Kim, "Super-resolution fluorescent materials: an insight into design and bioimaging applications", Chem. Soc. Rev., vol. 45, pp. 4651-4667, 2016. http://dx.doi.org/10.1039/C5CS00875A
  9. T.J. Chozinski, A.R. Halpern, H. Okawa, H. Kim, G.J. Tremel, R.O.L. Wong, and J.C. Vaughan, "Expansion microscopy with conventional antibodies and fluorescent proteins", Nature Methods, vol. 13, pp. 485-488, 2016. http://dx.doi.org/10.1038/nmeth.3833
  10. J. Seo, M. Choe, and S. Kim, "Clearing and Labeling Techniques for Large-Scale Biological Tissues", Molecules and Cells, vol. 39, pp. 439-446, 2016. http://dx.doi.org/10.14348/molcells.2016.0088
  11. Y. Li, T.D. Majarian, A.W. Naik, G.R. Johnson, and R.F. Murphy, "Point process models for localization and interdependence of punctate cellular structures", Cytometry, vol. 89, pp. 633-643, 2016. http://dx.doi.org/10.1002/cyto.a.22873
  12. M.D. Leonetti, S. Sekine, D. Kamiyama, J.S. Weissman, and B. Huang, "A scalable strategy for high-throughput GFP tagging of endogenous human proteins", Proceedings of the National Academy of Sciences, vol. 113, pp. E3501-E3508, 2016. http://dx.doi.org/10.1073/pnas.1606731113
  13. T. BRUNS, M. BAUER, S. BRUNS, H. MEYER, D. KUBIN, and H. SCHNECKENBURGER, "Miniaturized modules for light sheet microscopy with low chromatic aberration", Journal of Microscopy, 2016. http://dx.doi.org/10.1111/jmi.12439
  14. P.N. Hedde, and E. Gratton, "Selective plane illumination microscopy with a light sheet of uniform thickness formed by an electrically tunable lens", Microscopy Research and Technique, 2016. http://dx.doi.org/10.1002/jemt.22707
  15. A.P. Gorka, and M.J. Schnermann, "Harnessing cyanine photooxidation: from slowing photobleaching to near-IR uncaging", Current Opinion in Chemical Biology, vol. 33, pp. 117-125, 2016. http://dx.doi.org/10.1016/j.cbpa.2016.05.022

Destriping of Light Sheet data

We’ve been working on a simple, home-built light sheet system in the NIC. It’s designed for imaging cleared organs, and so uses a cylindrical lens to produce a light sheet, about the simplest illumination system you can use for such a microscope (it’s similar to the system described in [1]). Because the illumination traverses the sample, if there is an opaque or scattering part of the sample, it blocks part of the illumination beam, casting shadows through the sample that show up as stripes in the resulting images.

I recently discovered a software tool for removing stripes from these images [2]. It’s not perfect – in particular, it assumes that the noise is additive, when it is really multiplicative – but it does a good job. You can download a Fiji plugin that implements it here, and you can see the results below.

Raw image

Raw image

After destriping

After destriping

References

  1. H. Dodt, U. Leischner, A. Schierloh, N. Jährling, C.P. Mauch, K. Deininger, J.M. Deussing, M. Eder, W. Zieglgänsberger, and K. Becker, "Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain", Nature Methods, vol. 4, pp. 331-336, 2007. http://dx.doi.org/10.1038/nmeth1036
  2. J. Fehrenbach, P. Weiss, and C. Lorenzo, "Variational Algorithms to Remove Stationary Noise: Applications to Microscopy Imaging", IEEE Transactions on Image Processing, vol. 21, pp. 4420-4430, 2012. http://dx.doi.org/10.1109/TIP.2012.2206037