Launch HN: Biodock (YC W21) – Better microscopy image analysis

Hi Hacker News! We’re Nurlybek and Michael, the cofounders of Biodock (http://www.biodock.ai/). We help scientists expedite microscopy image analysis.

Michael and I built Biodock due to the challenges we experienced in microscopy image analysis while we were at Stanford. As a Ph.D. student, I spent hours manually counting through lipid droplets in microscope images of embryonic tissues. The incredible frustration I felt led me to try all kinds of software. Eventually, I went out to seek help from other scientists. Michael, a computer science student, was working in a lab just across from mine when he got my email asking for help. We got to chatting in a med school cafe and realized that we were both tackling the same issues with microscopy images.

Microscopy images are one of the most fundamental forms of data in biomedical research, from discovery all the way to clinical trials. They can be used to show the expression of genes, the progression of the disease, and the efficacy of treatments.

However, images are also very frustrating, and we think a lot of that has to do with the current tools available. To analyze their images, many scientists at top research institutions use software techniques invented 50 years ago, like thresholding and filtering. Some even spend their days manually drawing regions around cells or regions. Not only is this extremely frustrating, but it slows down the research cycle, meaning that it takes a lot more time and money to create potentially lifesaving cures. Contrast these tools to the incredible recent headway into deep learning - where applications like AlphaFold have led to incredible gains in what was previously possible.

Our goal is to bring these performance gains to research scientists. The current core module in Biodock is AI cell segmentation for fluorescent cells, based mostly on Mask R-CNN and U-Net architectures, and trained on thousands of cell images. Essentially, it identifies where each cell is and calculates important features like location, size, and fluorescent expression for each cell. This module performs around 40% more accurately than other software.

So how is this different from training deep learning models yourself? First, our pretrained modules are trained on a huge amount of data, which allows for great performance for all scientists without needing to label data or optimize training. Secondly, we’ve spent time carefully building our cloud architecture and algorithms for production, including a large cluster of GPUs. We even slice images into crops, process them in parallel, and stitch them together. We also have storage, data integrations, and visualizations built into the platform.

We know that AI cell segmentation addresses only a small fraction of microscopy analysis in the biomedical space, and we are launching several more modules soon, tackling some of the most difficult images in the space. So far, we’ve been able to generate different custom AI modules for diverse tissues and imaging modalities (fluorescence, brightfield, electron microscopy, histology). Eventually, we want to link other biological data analyses into the cloud including DNA sequences, proteomics, and flow cytometry, to power the 500K scientists and 3K companies in the US biotech and pharma space.

We would love to hear from you and get your feedback—especially if you've ever spent hours on image analysis!

To be fair, you can get really very far with non-machine-learning automated techniques (I did good-enough algorithms for an in-situ fluorescent gene sequencing image processing pipeline at one job). I suspect any form of good automated processing, regardless of whether it's AI, would be welcome to biologists.

If you'd ever like to chat about the automation parts, I'd be interested in hearing how you're approaching it; the niche of "scientific computing, but repeatable" is quite different than traditional scientific software, and it seems like people are still in early stages of figuring out how to do it.

Would also be interested in hearing how you approach correctness. The best approach I've discovered is metamorphic testing. Basically you modify real inputs, and then ensure the output matches. E.g. you say, "OK, I have this finished algorithm that segments cells, `f(image) -> cells`. Now, if I double brightness on everything, I would expect the same results, so let's see what `f(brighter_image)` is, I would expect same output." Or like "If I merge nuclei-looking splotches that cross what original segmentation boundary was, that should result in fewer cells." Unfortunately only discovered this technique after I left the image processing job, so haven't had chance to try it.

Definitely! There are some applications where traditional methods are just simply good enough. However, when they aren't, it can be incredibly frustrating. From our conversations with scientists, this kind of data (3D, histology, difficult tissues, new assays) is increasing in volume.

As for correctness, we've only done mAP scores and traditional accuracy metrics so far to compare with other algorithms, but we also have our own internal metrics and a test set we're building out in-house to cover many edge cases, many of which cover some of the things you are talking about. One thing we're always trying to be sensitive of is fairness. We want to make sure that we're not biasing the test to our algorithm, which would make us look better than we are.

Ah, yes. We're even looking to use some generative models in order to even do variations based on data and then compare that we do similarly well between cases.

I guess the point I was making was that we want to make sure we don't then use this generated or modified data in order to test other algorithms in the space and say we're better. Simply put, it would be unfair for us to make changes to perform better on a hurdle and then put other algorithms through those hurdles. But for internal use, it's definitely great!

Hi! We've been bootstrapping ariadne.ai [0] in the same space. Similar origin story, too - turning tools we built as grad students into products for the biomedical industry.

Looks like we're thinking along somewhat convergent lines, but with some interesting differences as well. Feel free to reach out to my HN username at ariadne.ai if you'd like to chat.

[0] https://ariadne.ai