New extension: Fully automatic whole-body CT segmentation in 2 minutes using TotalSegmentator

We know that the creators are working on including subcutaneous fat, the sternum bone, costovertebral connectors, and vessels. You may ask about specific structures in the TotalSegmentator GitHub Usage Issue and you could even train the software yourself or provide ground truth data to the creators because all training data are disclosed.

4 GB GPU memory is on the very low side of system requirements. See more details here.
So more GPU memory and RAM should help :slight_smile:

Minimum GPU memory requirement is 7GB.below that the segmentation may randomly fail on some data sets. For predictable outcomes, you need to switch to using CPU or upgrade your GPU.

Rotator cuff muscle injuries are so common that I’m sure there is enough interest in adding I to the TotalSegmentator model. If you segment this muscle on the TotalSegmentator datasets (they are all publicly available) and send them to Jakob Wassrthal (main developer of the TotalSegmentator model) then he will add it to the model.

Segmentation of a muscle in 2000 images may seem like a big effort, but if the muscle is well visible in CTs then it could be a very doable. You can use TotalSegmentator to segment each image then extract the relevant ROI based on the segmentation results (e.g., bounding box defined by a couple of segments). After that you can use MONAILabel to segment the muscle in each extracted image region. MONAILabel allows you to segment with adaptive learning, which means that you only need to segment a few dozen images from scratch, for the remaining you can get an automatic segmentation result that you just need to verify and fix up as needed. The automatic segmentation result will improve continuously with each completed case, so you should be able to finish the segmentation fairly quickly.


I am wondering if I can have chest wall and para spinal muscles segmentation by TotalSegmentator?

Similar/nearby structures around the chest wall and para spinal muscles are already segmented. Can you derive sufficient information from those? What is your clinical application?

In general the same answers apply to segmentation of any additional structure as given for the rotator cuff muscle question above, i.e., you need to segment a number of cases with manual/semi-automatic/adaptive learning tools and then that can be used to extend TotalSegmentator.

Thank you for replying.
I found paraspinal muscles segmentations.
I`ll try for the rest of muscles what you explained for Rotator Cuff.

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Hi Lassoan,

Very impressive work! Are you able to add option to smooth all the contours generated via fast option? Hoping this can reduce the jaggies?


Yes, you can apply smoothing in Segment Editor. However, that would not make the segmentation more accurate, so I would not recommend doing it (unless you just want to improve the look of the segmentation). If you want better image quality but you don’t have a GPU then you can get the full quality segmentation in 30-40 minutes on the CPU. It is fully automatic, so you just start it and it runs in the background.

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Hello, I have problems to install totalsegmentator, already I have read the document several times where, and I have made several attempts to install it without results. Can someone explain to me how to install it? a tutorial video?

Please describe what you did, what you expected to happen, and what happened instead. Include screenshots and copy here all messages displayed in the log window of the TotalSegmentator module.

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5 posts were split to a new topic: Segment the body surface using sTotalSegmentator

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Will this plugin use our local weights to compute(refined or trained by my local dataset), and the weights uploaded to googledrive for users to download

2 posts were split to a new topic: TotalSegmentator indicates that GTX 1060 GPU has not enough memory

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