# Help to Segment Multiple Sclerosis Plaques with MONAI Label

**URL:** <https://discourse.slicer.org/t/help-to-segment-multiple-sclerosis-plaques-with-monai-label/34983>\
**Category:** Support\
**Tags:** segmentation, monailabel\
**Created:** [March 19, 2024, 5:39pm UTC](https://discourse.slicer.org/t/help-to-segment-multiple-sclerosis-plaques-with-monai-label/34983 "2024-03-19T17:39:49Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![franbuemi](https://sea2.discourse-cdn.com/flex002/user_avatar/discourse.slicer.org/franbuemi/32/67915_2.png) [@franbuemi](https://discourse.slicer.org/u/franbuemi)\
**Post date:** [March 19, 2024, 5:39pm UTC](https://discourse.slicer.org/t/help-to-segment-multiple-sclerosis-plaques-with-monai-label/34983/1 "2024-03-19T17:39:49Z")

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Hi everyone, I would like to train a model to segment multiple sclerosis plaques on 3D FLAIR images, one by one, each with distinct labels. I’ve started with a dataset of 10 patients just to get started before tackling segmentation on a larger number of cases. As described in the tutorial “MONAI Label - Training from Scratch ([https://www.youtube.com/watch?v=3HTh2dqZqew](https://www.youtube.com/watch?v=3HTh2dqZqew))”, I modified the dictionary in the deepedit.py file by adding labels like “lesion1, lesion2, lesion3,” etc. (I’ve included 7 labels) and then changed “use\_pretrained\_model” to “false”. As seen in the screenshot, I’ve just reached an accuracy of 27%. However, even after adding additional labels for lesions in the segment editor (such as lesion8, lesion9, lesion10, etc., since some cases have more than 7 plaques), it seems that lesions beyond the seventh one are not considered during training (see the terminal screenshot). How can I resolve these issues? Would training on more cases lead to better accuracy?

 ![MS1](https://us1.discourse-cdn.com/flex002/uploads/slicer/original/3X/9/d/9dc846480e6eacbffab25ef6c189ee43d4006205.jpeg)

 ![MS2](https://us1.discourse-cdn.com/flex002/uploads/slicer/original/3X/5/8/5897a8100c060621f67a88e15ba2bdbb0d564a6b.png)