Hi everyone,
I’m currently working with Prof. Ron Alkalay on a spine CT segmentation project using MONAI Auto3DSeg and would appreciate any advice from the community.
My task is to segment only vertebral bodies. I reformulated it as a binary segmentation task (VB vs. non-VB), which initially improved the validation Dice to >70%.
However, when I ran a second round of training, the performance dropped noticeably and did not recover, even though the training process itself ran normally.
Between the two runs, I changed the following parameters:
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Resample resolution
From: (0.3125, 0.3125, 0.5)
To: (0.3125, 0.3125, 1.0) -
ROI size
From: (128, 128, 64)
To: (128, 128, 96)
Other than these changes, the setup and data split were kept the same.
In both experiments, I used Auto3DSeg’s Quick training mode.
Dataset details
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imagesTr: ~60 GB, 257 CT volumes (.nii.gz) -
labelsTr_bin: ~385 MB, 257 binary segmentation masks (.nii.gz)
Some additional details:
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Tool: MONAI Auto3DSeg - segresnet_0
-
Task: Vertebral body vs. background (binary)
-
Modality: CT
-
Tried adjusting: ROI size, AMP, batch/auto-scaling
I’m trying to understand whether this performance drop is likely related to the coarser through-plane resolution, the larger ROI depth, Quick mode limitations, or some interaction with Auto3DSeg’s preprocessing and model selection.
If anyone has experience using Auto3DSeg for similar anatomical segmentation tasks, I would really appreciate any guidance on what to sanity-check first, or best practices for tuning these parameters.
I’m happy to share configs or logs if helpful.
Thank you very much in advance!