/r/medical_imaging
This is a subreddit to discuss the algorithms, physics, hardware, and results of medical imaging. Feel free to ask for advice on algorithms, ask for help in understanding the physics, discuss recent developments, or anything else. Also, discussions of ways to proliferate medical imaging technologies to impoverished nations are very appropriate. Finally, discussions of how to teach medical imaging are welcome!
This subreddit is not a place to submit your images and ask for a diagnosis. We are here to improve medical imaging, not to provide medical advice.
Other relevant subreddits: r/Medical_Students /r/MedicalPhysics
/r/medical_imaging
1,510 Subscribers
How To Verify Accuracy of FreeSurfer Cortical Reconstructions
I have been working with Freesurfer running reconstructions on Mp2rage MRI data for an internship, and while I think I have been able to figure out how to correctly skullstrip the noisy Mp2Rage image, the professor I am doing the internship with has said that the aseg and aparc results of the reconstruction are not totally accurate. I'm not sure why, although I suspect it's because the skullstrip still has some noise around the edges. I'm going to try brainvoyager or premade matlab scripts to denoise the mp2rage image rather than doing it manually by multiplying the mask generated for the skullstrip of the inversion pulse with the original image.
However, even if I figure out this issue I will still need a way to check my results. I am able to tell that the major structures of the brain are in the right place, but the professor told me this is not enough to verify that the reconstruction is an accurate representation of the original MRI data (which makes sense). I don't know how he's able to tell that the reconstruction is inaccurate by looking at the segmentation, but he wants me to actually run some type of analysis to figure out how to check the data rather than asking him.
He is being intentionally vague because he wants me to figure out how to do this without his help, but I am pretty stuck. He mentioned something about statistical analysis, but it wasn't clear what he meant. I am looking for any tips to mathematically verify that the reconstructions are accurate, and also possibly ways to fix inaccuracies that aren't too extreme.
18:06 UTC
New Medical Imaging News supplement to Computer Vision News
Dear all,
Awesome R&D content (with code!) on Computer Vision News of February 2022
In particular, members of this group will love the special supplement - Medical Imaging News - starting on page 23.
Dilbert on page 2. Get it every month: free subscription on page 56.
Enjoy!
10:35 UTC
Transformers in Medical Imaging: A Survey
- Excited to share our latest survey paper on the applications of Transformer models in Medical Imaging by covering more than 125 papers and a diverse set of applications including segmentation, detection, classification, registration, reconstruction, and clinical report generation.
- For paper and related github repository please check https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging
21:41 UTC
The diversity problem plaguing the Machine Learning community
08:30 UTC
Improving Image Quality by Utilizing the Wavelet Transform's Structure with Compressed Sensing.
04:33 UTC
Any of you guys seen your work translated to clinical practice?
04:08 UTC
Dicom viewer for deep learning models clinical validation
hi , we as a team developing AI health care product assisting the radiologist in the workflow. For validatibg our deep models upto now we used manual segregation and hard coded segmentation of anaomaly on jpeg images for the visualization. Now we want move to dicom where the segmentation contours and blues will be dropped onto dicom tags . is there any dicom viewer which can read these dicom tags and display the annotation results with a toggle button . We're interested in the sharing the feedback over the dicom platform which will be embedded into the dicom tags.
please suggest any dicom viewer that are relevant to our requirements.
10:48 UTC
The Troublesome Kernel: Showing that Machine Learning Will Always be Unstable
18:26 UTC
Parahydrogen MRI
Does anyone know how long it is likely to be before this technology can be used to scan patients? Also, is it likely to be an improvement on current MRI (i.e. higher resolution), or just cheaper?
​
https://www.healthimaging.com/topics/ai-emerging-technologies/significant-breakthrough-mri
02:18 UTC
Physicist explains MRI physics.
04:17 UTC
Awesome medical imaging papers/tools on Computer Vision News (with code!) - August 2021
Dear all,
Have a peek at Computer Vision News of August.
Many articles about Medical Imaging, including the reviews of the Best Paper Awards at the MIDL (Medical Imaging with Deep Learning) conference held in July...
Dilbert on page 2. Free subscription on page 44.
Enjoy!
14:19 UTC