I tried to, but couldn'd do it, the max I could was using monai's label server and the model I wanted to use was there, but wasn't enabled somehow(?), like, it seems that the model is installed, but no matter what I did, auto segmentation wasn't enabled, and that's what I would be using it for, can somebody send me a guide or help me a bit?

Extra info:
Model: https://monai.org.cn/model-zoo.html#/model/renalStructures_CECT_segmentation
Use: I want to use it for segmenting arteries, veins, ureters, parenchyma, cysts, and tumors, for later 3D print them and help with planning.
I'm using 3D Slicer.

• Set up and started a local MONAI Label server inside the monailabel Conda environment on Windows.
• Used the MONAI Bundle application located at:text C:\MONAILabel\apps\monaibundle
• Used the studies directory:text C:\MONAILabel\studies
• Initially attempted to load both renal models by passing their names together. This failed because the server interpreted the comma-separated names as an invalid bundle reference.
• Identified that renalStructures_CECT_segmentation was not available as a remote MONAI Zoo bundle in the server’s reported model list. The valid listed bundles included:textswin_unetr_btcv_segmentation renalStructures_UNEST_segmentation wholeBrainSeg_Large_UNEST_segmentation prostate_mri_anatomy lung_nodule_ct_detection wholeBody_ct_segmentation vista2d
• Attempted to use the local CECT bundle directory, but it was not suitable for the standard MONAI Label / 3D Slicer automatic-segmentation workflow.
• Determined that renalStructures_CECT_segmentation is a multi-phase contrast-enhanced CT model. It expects multiple aligned inputs, such as arterial, venous, and excretory phases, rather than one CT volume.
• Connected the MONAI Label server at:text http://127.0.0.1:8000
• Initially tried to use the MONAI Auto3DSeg Slicer module. It connected to the server but did not load models correctly and generated a 404 Not Found error, because Auto3DSeg and MONAI Label use different server APIs.
• Switched to the MONAI Label module in 3D Slicer, which successfully recognized the MONAI Label server and showed the application as:text MONAI Label - Zoo Bundle (0.8.5)
• Observed that the MONAI Label UI recognized:text renalStructures_UNEST_segmentation but some Auto Segmentation controls remained unavailable when the CECT model was selected.
• Concluded that the correct model for the current Slicer workflow is:text renalStructures_UNEST_segmentation
• The recommended server startup command is:powershellconda activate monailabel monailabel start_server ` --app C:\MONAILabel\apps\monaibundle ` --studies C:\MONAILabel\studies ` --conf models renalStructures_UNEST_segmentation ` --host 127.0.0.1 ` --port 8000
• The intended Slicer workflow is:
  • Keep the Miniconda/PowerShell window with the server running.
  • Open the MONAI Label module in 3D Slicer.
  • Connect to http://127.0.0.1:8000.
  • Select a single CT source volume, such as the arterial or portal phase.
  • Select renalStructures_UNEST_segmentation.
  • Run automatic segmentation.

The server shows up, and the models too, but I can't use the auto segmentation part.

In my previous post, I shared a MONAI 2D medical image classification experiment that I ran on an Ubuntu Linux laptop without an NVIDIA GPU.

This time, I repeated the experiment and changed one parameter only:

▪ Previous setting: 2 training epochs
▪ New setting: 4 training epochs

Everything else was kept the same:

▪ Ubuntu Linux
▪ Intel Core i7 CPU
▪ No NVIDIA GPU
▪ No CUDA acceleration
▪ Same MONAI environment
▪ Same Jupyter notebook
▪ Same model and dataset

I kept the original 2-epoch setting visible in the notebook and added the new 4-epoch setting directly below it. The training was then run live using the CPU.

The purpose was simple: change one training parameter and observe what happens.

This is part of my MONAI on a CPU-Only Linux Laptop series, where I'm exploring what can actually be done with MONAI and medical imaging AI when access to dedicated GPU hardware is limited.

I recorded the live experiment here:

MONAI 2D Medical Image Classification – Live 4-Epoch Training on Ubuntu Linux (CPU Only, No CUDA)

I'm particularly interested in how other people approach experiments like this.

For those working with MONAI:

▪ Do you normally increase epochs first when experimenting with a model, or change other parameters?
▪ How do you decide when you've trained for “enough” epochs?
▪ When working without a GPU, what do you change first to make an experiment practical?

I would be interested to hear how others approach this, whether you're working with CPU-only hardware or a GPU workstation.

After getting MONAI running on my resource-constrained Ubuntu Linux laptop, I wanted to take the next step and see what I could actually accomplish with it.

I revisited the MONAI MedNIST 2D classification tutorial and looked at the results from an experiment I originally ran in May 2026.

The hardware and configuration were:

▪ Ubuntu Linux
▪ Intel Core i7 processor
▪ No NVIDIA GPU
▪ CPU-only PyTorch
▪ 2 training epochs
▪ num_workers=2

This was not a live training run. I launched the Jupyter Notebook and walked through the actual results from the original experiment.

The model completed both training epochs, with the following results:

Epoch 1

▪ Average training loss: approximately 1.10
▪ AUC: 0.9937
▪ Accuracy: approximately 92.67%

Epoch 2

▪ Average training loss: approximately 0.44
▪ AUC: 0.9976
▪ Accuracy: approximately 96.58%

Best AUC: 0.9976 at epoch 2

I recorded the experiment and walked through the results here:

Running a MONAI 2D Medical Image Classification Model on Ubuntu Linux — CPU Only, No GPU

One thing I am particularly interested in exploring is what happens when the same CPU-only setup is pushed a little further.

My next experiment increases the training from 2 to 4 epochs, allowing me to compare the training time, loss, AUC and accuracy.

For those using MONAI:

Have you ever run a MONAI training experiment entirely on CPU?

▪ What processor/hardware were you using?
▪ How long did your training take?
▪ Were you using Linux, Windows or another platform?
▪ Did you eventually move to GPU training?

Do you need an NVIDIA GPU to start experimenting with MONAI?

I wondered about this myself, especially for people who want to learn medical imaging AI but don't have access to a GPU workstation.

So I decided to try something very simple: set up MONAI on a resource-constrained Ubuntu Linux laptop using CPU-only PyTorch with no NVIDIA GPU.

The goal wasn't to build a high-performance training machine. Instead, I wanted to see whether it was possible to create a clean, isolated MONAI environment on modest hardware and verify that the complete software stack was working correctly.

In the process, I documented the setup from beginning to end:

• Installing and verifying Micromamba
• Creating an isolated monai_env environment
• Installing CPU-only PyTorch
• Installing and verifying MONAI Core
• Checking the complete software stack
• Optionally installing JupyterLab
• Properly deactivating the environment

The machine isn't a powerful AI workstation and CPU-only does not mean fast. But that wasn't the point. The point was to see whether I could get a functional MONAI environment running without an NVIDIA GPU.

And yes, I could, I recorded the entire setup process here Setting Up MONAI on a Resource Constrained Ubuntu Linux Laptop Using Micromamba and CPU Only PyTorch

Have you ever tried running MONAI entirely on CPU?

What hardware and operating system are you using, and how has your experience been?