# Air segmentation using MONAILabel\_improve accuracy

**URL:** <https://discourse.slicer.org/t/air-segmentation-using-monailabel-improve-accuracy/29482>\
**Category:** Support\
**Tags:** segmentation, mri, monailabel\
**Created:** [May 15, 2023, 10:11pm UTC](https://discourse.slicer.org/t/air-segmentation-using-monailabel-improve-accuracy/29482 "2023-05-15T22:11:20Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Spiros\_Karkavitsas](https://sea2.discourse-cdn.com/flex002/user_avatar/discourse.slicer.org/spiros_karkavitsas/32/18224_2.png) [@Spiros\_Karkavitsas](https://discourse.slicer.org/u/Spiros_Karkavitsas)\
**Post date:** [May 15, 2023, 10:11pm UTC](https://discourse.slicer.org/t/air-segmentation-using-monailabel-improve-accuracy/29482/1 "2023-05-15T22:11:20Z")

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Hello everyone

Currently, I am trying to use MONAILabel to train a model for air segmentation of MR images in the pelvic region. The approach I used was : 1) Create an already air annotated dataset  
2) Use the radiology app and train a model from scratch.

However, the model accuracy using the default settings (e.g. random active strategy) is limited in 0.7 % which is low in my case.

Do you know any more detailed way of increasing the model performance? One way I thought would be to create more annotated data and use it a training dataset. However, this is time consuming.  
What do you suggest?

Thank you for your time in this.