4 ms·

I am an emergency physician without any formal software training and for the last three months I’ve been trying to build a program that segments the wall of the

I am an emergency physician without any formal software training and for the last three months I’ve been trying to build a program that segments the wall of the heart from an ultrasound video and then identifies regions that aren’t moving (an early sign of heart attack). There are many similarities between this man’s project and mine. And if I had his knowledge I may have cracked my problem by now and would have new way to detect heart attacks early.

newsbinator 6y ago
Please post your contact info in your profile so that folks can reach you. Hopefully one of us can help.
- floatingatoll 6y ago
  OP is the author of the post, in case that helps any.
- davycro 6y ago
  Done. Appreciate it!
TaylorAlexander 6y ago
In addition to posting your contact info please also check out this course designed for people from other fields who want to learn machine learning. https://www.fast.ai/ https://www.fast.ai/
- TaylorAlexander 6y ago
  Wow I sometimes I understand the downvotes but this one doesn’t make any sense...
  - tln 6y ago
    Agreed! Thanks for posting this
- davycro 6y ago
  Fast.Ai is incredible. Love it. I've bumbled my way through it and was able to make a unet learner trained from this dataset -- https://www.creatis.insa-lyon.fr/Challenge/camus/ https://www.creatis.insa-lyon.fr/Challenge/camus/ -- in order to segment the left ventricle from an apical four chamber view. It's a start, but I still have a long way to go.
  - TaylorAlexander 6y ago
    Awesome!
enriquto 6y ago
Do you realize that there are thousands of scientific publications in medical image processing that address exactly the same concrete problem that you have?
- xwdv 6y ago
  "Not invented here".
- davycro 6y ago
  Yes, however much of that research is for formal ultrasound obtained by a professional sonographer with an expensive machine. I'm interested in bedside ultrasound performed by an emergency physician with a mediocre machine (eg butterfly). It seems the primary way to detect regional wall motion abnormalities is with speckle tracking, which requires way too much post-processing for a clinician. A system that segments the left ventricle and finds akinetic regions in realtime from a parasternal long axis view or an apical four chamber view would be pretty nifty. If you know of a paper or system that does this now then please let me know. I would love for someone else to have solved this, haha. My email is Davidm.Crockett [at] Utah.edu
  - jiggawatts 6y ago
    I love this comment deeply, because it is a view into someone else's world, someone who is chipping away at real problems and making "the future" happen. A future where more people are saved from death through incredible-yet-easy-to-use technology. Kudos for advancing the human race.
jedimastert 6y ago
If you have any examples of what you're talking about, can you send them to me? That sounds like something I'd love to poke at. My email is my username at gmail.
janderson3 6y ago
This paper might be useful [0]. It measures the thickness of a tissue. If you do that over time you can detect the parts that aren't moving. I studied under Yezzi for a while. Cool dude. [0] http://iacl.ece.jhu.edu/~prince/pubs/2003-TMI-Yezzi-Thickness.pdf http://iacl.ece.jhu.edu/~prince/pubs/2003-TMI-Yezzi-Thicknes...
- davycro 6y ago
  Awesome, thank you for the reference. That's the essence of finding regional wall motion abornomalities. When looking at the ultrasound I look for parts of the myocardium that don't change in size relative to their neighbors during the cardiac cycle.
fpgaminer 6y ago
Correct me if I'm wrong (I haven't worked with health data science like this) ... wouldn't an end-to-end approach work better from the get-go? What I mean is, rather than developing a segmentation algorithm and then a motion detection algorithm, why not just feed a bunch of frames into a CNN and have it directly predict "heart attack risk"? Or is the segment-then-motion-detect approach necessary because of its better explainability? I guess I view the end-to-end approach as being less fiddly than the more traditional computational imaging approach. And it has a bonus. If data is available, you could feed it historical ultrasound data from patients that later had heart attacks. With that, it's possible it will learn other features of an ultrasound that predict future heart attack.
- davycro 6y ago
  That would require a dataset of ultrasounds from people having active myocardial infarctions, which we don’t have, and would take at least a year of academic coordination to assemble. The current datasets are just labeled anatomy at end systole and diastole.
  - fpgaminer 6y ago
    Hmm, curious. I suppose I would wonder, then, if there's no dataset how any algorithm could be developed. What I mean is ... once you've developed something, how do you test how well it performs? How do you analyze the effectiveness of the algorithm, the false positive rate, etc.
    - davycro 6y ago
      I could assemble a small dataset of less than a hundred ultrasounds to test the algorithm. A big dataset that could train an AI would require quite the effort. Great questions, and you highlight the need for shared ultrasound data.
deleted 6y ago
[deleted]
QuadmasterXLII 6y ago
Hi! You should consider contacting Stephen Aylward at Kitware. His corner of the company specializes in connecting clinicians with expertise in point of care ultrasound with machine learning programmers paid through Kitware, funded by NIH grants. I worked for him for the last three years, focused on assessing intracranial pressure with ultrasound and assessing pneumothorax with ultrasound.
- davycro 6y ago
  Heck yes, will do! For intracranial pressure did you look at ocular ultrasound? How did that work out? Did you have ICPs from actual bolted patients for the gold standard / ground truth? That would be incredibly useful especially in patients on a ventilator who can’t provide a neuro exam. Pneumothorax seems tricky since really it requires a lung point for diagnosis, and those can be hard to find. Did you simply look for lung sliding? That’s all a physician really needs to make an informed diagnosis. I love hearing about this. Thank you for the work you do

newsbinator 6y ago

Please post your contact info in your profile so that folks can reach you. Hopefully one of us can help.

floatingatoll 6y ago
OP is the author of the post, in case that helps any.
davycro 6y ago
Done. Appreciate it!

TaylorAlexander 6y ago

In addition to posting your contact info please also check out this course designed for people from other fields who want to learn machine learning. https://www.fast.ai/ https://www.fast.ai/

TaylorAlexander 6y ago
Wow I sometimes I understand the downvotes but this one doesn’t make any sense...
- tln 6y ago
  Agreed! Thanks for posting this
davycro 6y ago
Fast.Ai is incredible. Love it. I've bumbled my way through it and was able to make a unet learner trained from this dataset -- https://www.creatis.insa-lyon.fr/Challenge/camus/ https://www.creatis.insa-lyon.fr/Challenge/camus/ -- in order to segment the left ventricle from an apical four chamber view. It's a start, but I still have a long way to go.
- TaylorAlexander 6y ago
  Awesome!

enriquto 6y ago

Do you realize that there are thousands of scientific publications in medical image processing that address exactly the same concrete problem that you have?

xwdv 6y ago
"Not invented here".
davycro 6y ago
Yes, however much of that research is for formal ultrasound obtained by a professional sonographer with an expensive machine. I'm interested in bedside ultrasound performed by an emergency physician with a mediocre machine (eg butterfly). It seems the primary way to detect regional wall motion abnormalities is with speckle tracking, which requires way too much post-processing for a clinician. A system that segments the left ventricle and finds akinetic regions in realtime from a parasternal long axis view or an apical four chamber view would be pretty nifty. If you know of a paper or system that does this now then please let me know. I would love for someone else to have solved this, haha. My email is Davidm.Crockett [at] Utah.edu
- jiggawatts 6y ago
  I love this comment deeply, because it is a view into someone else's world, someone who is chipping away at real problems and making "the future" happen. A future where more people are saved from death through incredible-yet-easy-to-use technology. Kudos for advancing the human race.

jedimastert 6y ago

If you have any examples of what you're talking about, can you send them to me? That sounds like something I'd love to poke at. My email is my username at gmail.

janderson3 6y ago

This paper might be useful [0]. It measures the thickness of a tissue. If you do that over time you can detect the parts that aren't moving. I studied under Yezzi for a while. Cool dude. [0] http://iacl.ece.jhu.edu/~prince/pubs/2003-TMI-Yezzi-Thickness.pdf http://iacl.ece.jhu.edu/~prince/pubs/2003-TMI-Yezzi-Thicknes...

davycro 6y ago
Awesome, thank you for the reference. That's the essence of finding regional wall motion abornomalities. When looking at the ultrasound I look for parts of the myocardium that don't change in size relative to their neighbors during the cardiac cycle.

fpgaminer 6y ago

Correct me if I'm wrong (I haven't worked with health data science like this) ... wouldn't an end-to-end approach work better from the get-go? What I mean is, rather than developing a segmentation algorithm and then a motion detection algorithm, why not just feed a bunch of frames into a CNN and have it directly predict "heart attack risk"? Or is the segment-then-motion-detect approach necessary because of its better explainability? I guess I view the end-to-end approach as being less fiddly than the more traditional computational imaging approach. And it has a bonus. If data is available, you could feed it historical ultrasound data from patients that later had heart attacks. With that, it's possible it will learn other features of an ultrasound that predict future heart attack.

davycro 6y ago
That would require a dataset of ultrasounds from people having active myocardial infarctions, which we don’t have, and would take at least a year of academic coordination to assemble. The current datasets are just labeled anatomy at end systole and diastole.
- fpgaminer 6y ago
  Hmm, curious. I suppose I would wonder, then, if there's no dataset how any algorithm could be developed. What I mean is ... once you've developed something, how do you test how well it performs? How do you analyze the effectiveness of the algorithm, the false positive rate, etc.
  - davycro 6y ago
    I could assemble a small dataset of less than a hundred ultrasounds to test the algorithm. A big dataset that could train an AI would require quite the effort. Great questions, and you highlight the need for shared ultrasound data.

deleted 6y ago

[deleted]

QuadmasterXLII 6y ago

Hi! You should consider contacting Stephen Aylward at Kitware. His corner of the company specializes in connecting clinicians with expertise in point of care ultrasound with machine learning programmers paid through Kitware, funded by NIH grants. I worked for him for the last three years, focused on assessing intracranial pressure with ultrasound and assessing pneumothorax with ultrasound.

davycro 6y ago
Heck yes, will do! For intracranial pressure did you look at ocular ultrasound? How did that work out? Did you have ICPs from actual bolted patients for the gold standard / ground truth? That would be incredibly useful especially in patients on a ventilator who can’t provide a neuro exam. Pneumothorax seems tricky since really it requires a lung point for diagnosis, and those can be hard to find. Did you simply look for lung sliding? That’s all a physician really needs to make an informed diagnosis. I love hearing about this. Thank you for the work you do