As someone with software background and interested in bio, it's a real pleasure to see a commentary from an experienced practitioner.
While we're discussing venues of progress, it's clear that software (and deep learning advances specifically) is poised to have a large impact on how bio research is conducted, and what categories of questions we'll be able to answer. The current consensus on how you leverage software in practice is to put both bio and software teams under one roof (Insitro and Recursion are canonical examples). I wonder if you think software-only company makes sense in this space? The analogy I like to use: people used to roll out their own accounting software within large enterprises until spreadsheets came along. Is there room for an equivalent in some segment of bio?
Excited to see you launching this! I agree on the basic premise: existing tools for segmentation labeling leave copious room for an improvement.
I just gave Segments a spin with an image data I work on at the moment. First impressions:
1. When trying to connect segments (by dragging), I seem to lose the original segment
2. Your model seems to be confused by noisy data that I happened to upload - it's a microscopy image. To a human eye it's quite clear what the areas of interest are.
1. If the segment you start dragging from is already selected, all the segments you drag through will get deselected, and vice versa.
2. Did you try changing the granularity of the segments by scrolling your mouse wheel? We've had good experiences with microscopic imagery before, happy to connect and dig a bit deeper.
1. Oh, I see. I didn't guess that's the intended behaviour. I wonder if it's not too clever.
2. Yes, then segments get too "excited" about the background noise. I would be able to make it work but with loads of manual tweaking which is, as I understand, the pain Segments wants to alleviate.
The segments you see on the screen are generated by our ML model. If your data is very noisy, our out-of-the-box model might not be the best fit. We can always improve performance by training a custom model for you on a small set of manually labeled data though.
Do I read it correctly that you're working with images with repeated pattern of instances of the same object on the image? I've been working with cell images, solving segmentation task - what biodock works on - and found interesting tricks to train models on vastly smaller number of labels than what you would think is possible with off-the-shelf models (e.g. Mask-RCNN or U-net + refinements).
While we're discussing venues of progress, it's clear that software (and deep learning advances specifically) is poised to have a large impact on how bio research is conducted, and what categories of questions we'll be able to answer. The current consensus on how you leverage software in practice is to put both bio and software teams under one roof (Insitro and Recursion are canonical examples). I wonder if you think software-only company makes sense in this space? The analogy I like to use: people used to roll out their own accounting software within large enterprises until spreadsheets came along. Is there room for an equivalent in some segment of bio?