MONAI Auto3DSeg – inconsistent performance for vertebral body segmentation

jchao
Fri, 06 Feb 2026 21:23:41 +0000
Support

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:

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

imagesTr: ~60 GB, 257 CT volumes (.nii.gz)

labelsTr_bin: ~385 MB, 257 binary segmentation masks (.nii.gz)

Some additional details:

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!

2 posts - 2 participants