"In today’s AI research, any project lasting longer than 6 months is considered slow, if not outdated and overrun by history. RoboEM, with a concept and an approach that seem quite plausible, still took 5 years from idea to the fully evaluated tool reported here. This was only possible with the patient support of the Max Planck Society, which encourages long-term projects, and the tenacity of the first author. Details made all the difference. And, as often, dead ends had to be avoided efficiently. We had to abandon, for example, the idea of using steering variability to indicate branch points — a nice analogy to road intersections in car steering, but too far-fetched to work well enough in brain tissue data. Sometimes giving up beloved ideas is as important as following through on others. M.H."
Yeah! Our protocol for segmentation, analysis and visualization of #volumeEM data is now published in
NatureProtocols. Thank you to all colleagues and reviewers.
Here is the link: https://rdcu.be/dzYGO
Our new EU PhD Network #ZooCell is hiring! We have 12 PhD positions in Germany, France, UK, Italy and Sweden.
The broad topic is evolution of sensory cell types in animal diversity: multidisciplinary training in 3D cellular reconstruction (volume EM), multimodal data analysis and science outreach.
"Compared with the honeybee and the fruit fly, Megaphragma exhibits the following miniaturization-related adaptations: a significant reduction in the number of ommatidia, absence of several cell types, reduced size, and denucleation of neurons. Interestingly, the reduction in lens diameter is less than that expected from the optimization of the optical resolution of the eye. This suggests that light sensitivity is a more important
consideration when lens diameter approaches the wavelength of light. The absence of wide-field (or non-columnar) lamina neurons in Megaphragma could be a consequence of the smaller number of ommatidia, their larger acceptance angle, and the lower resolving power of the eye."
Volume assembled with #FijiSc and #TrakEM2, and its neurons and synapses mapped with #CATMAID. Woohoo!
Unprecedented nanometer-scale structures of cristae in >400 individual #mitochondria, by @Hirabayashi_Lab, using a novel deep learning-based analysis of #volumeEM images, (also reveals previously unknown function of OPA1) #PLOSBiologyhttps://plos.io/3OXtPzK
"Volume EM: a quiet revolution takes shape" – a review and commentary by Lucy Collinson et al. 2023 on present and future electron microscopy technology and its application https://www.nature.com/articles/s41592-023-01861-8
Following from Collinson's paper, here is my take on scaling up volume electron microscopy for connectomics in the #UK or any country willing to commit about 10 to 20 million a year:
"The (re)appreciation that true insight into neural function depends reciprocally on an in-depth understanding of the underlying structure(s) has driven a surge in efforts to develop novel imaging techniques. In fact, keeping track of these approaches, which continue to emerge at a staggering pace, has become a challenge by itself." #neuroscience@PLOSBiology
For any #3DEM#volumeEM people: is there an alternative software for skinning that you can recommend?
I’m building a 3D model of a complex subcellular structure, using 3DMOD. During skinning, imodmesh is generating a many misconnections. I've minimised them but I’m wondering if other software has better algorithms for generating the mesh.