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Hello everyone! I'm posting our research work as you might be interested in how we used ML to map part of the brain cells of the human hippocampus :) We used various human brain slices at high resolution (1 micrometer per pixel) and developed a custom segmentation pipeline that uses SoTA whole slice cell segmentation networks, like CellPoseSAM with good zero shot performances. We then refined semi-automatically those annotations and ensembled more finetuned models within the pipeline, adding a merging algorithm and a cell classification for 3 classes (excitatory and inhibitory neurons, and glial cells). But the high-res slices covered only a few parts of the hippocampus with respect to other slices scanned at 20x less the resolution where the cell nuclei are only 1 pixel wide. So we tried to map the high-res annotations we obtained to the low-res corresponding slices, and used a small UNet to supervise a density estimation task for 3 classes. We obtained a network that outputs a density map that can be sampled to obtain a probabilistic map of the cellular positions. Finally, to reconstruct the volume, we stacked together all the low-resolution density maps from all the slices that covered the hippocampus and obtained a point cloud, which you can see in the GIF along the corresponding anatomical CA (Cornus Ammonis) areas. The performances are still limited by the quantity of data and low-resolution slices, but we showed that the results were biologically plausible given previous estimates by other researchers. The paper was accepted at MICCAI 2026 a few weeks ago! Feedback is very welcome, especially on the density-estimation formulation and possible uses of the generated point cloud. submitted by /u/V_ector [link] [Kommentare]
Hi everyone, I'm building the original InMoov v1 robotic hand . The mechanical assembly and servo control are working well, but I'm currently stuck with the sensor integration. My goal is to add: Hall Effect sensors for finger position/feedback. FSR (Force Sensitive Resistor) sensors on the fingertips for touch and grip force detection. Ultimately, I want to implement closed-loop gripping, where the hand can detect contact with an object and adjust the grip force instead of simply moving the servos to fixed positions. The problem is that I can't find a proper guide explaining how to do this. I've spent a lot of time searching through YouTube, Google, GitHub, forums, and other online resources, but I still haven't found a complete tutorial that covers the entire process. I'm specifically looking for help with: Where to mount the Hall sensors and magnets. How to mount the FSR sensors on the fingertips. Wiring diagrams. Arduino code/examples. Sensor calibration. How to combine the Hall sensors, FSRs, and servo control into one working system. If anyone has: YouTube videos GitHub repositories Research papers Wiring diagrams Build logs Arduino examples Personal experience integrating these sensors into an InMoov hand (or any tendon-driven robotic hand) I would really appreciate your help. I've been stuck on this for quite some time, so any guidance or resources would mean a lot. Thank you! submitted by /u/Odd-Bell1718 [link] [Kommentare]
