---
title: "Viseg segmentation inference"
description: "Run viseg anatomy/lesion inference using VisegInferenceModel, binarize_mask, and ClearML model registry (LATEST_ANATOMY_MODEL_ID). Points to vxData SDK for data access."
image: "https://docs.virdx.dev/img/virdx-social-card.png"
---

> Documentation Index
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> Use this file to discover all available pages before exploring further.

# Viseg segmentation inference

## Current API (verified 2026-08-06)

```python
from viseg.inference import VisegInferenceModel, binarize_mask
from viseg import LATEST_ANATOMY_MODEL_ID
import torch

# Load model from ClearML registry
model = VisegInferenceModel(
model_id=LATEST_ANATOMY_MODEL_ID,  # "71aec22bf3044253b83a935b33141356" as of 2026-04-27
device=torch.device("cuda:0"),
n_test_flips=1,          # test-time augmentation (0=off, max 7)
compute_entropy=True,
)

# Anatomy (1 channel: T2)
seg_vol, probs_vol, uncertainty_dict = model.predict_sample([t2_volume])

# Lesion (3+ channels: T2 + ADC + DWI, optionally + anatomy_mask)
seg_vol, probs_vol, uncertainty_dict = model.predict_sample(
[t2_vol, adc_vol, dwi_vol],
crop_mask=binary_prostate_mask,  # optional
)
```

**Returns:**

- `seg_vol` — `vicom.Volume`, integer class labels `[D, H, W]`
- `probs_vol` — `vicom.Volume`, softmax probabilities `[N_classes, D, H, W]`
- `uncertainty_dict` — `dict[str, Volume]`: `"entropy"` and/or `"mutual_info"` (mutual_info requires ensemble)

## Segmentation labels (anatomy task)

```
0: background       5: pz (peripheral zone)
1: bladder_lumen    6: tz (transition zone)
2: bladder_wall     7: as (anterior stroma)
3: urethra          8: cz (central zone)
4: rectum           9: neurovascular_bundles
               10: seminal_vesicles
```

## Binary prostate mask

```python
from viseg.inference import binarize_mask

binary = binarize_mask(
seg_vol,
foreground_classes=[5, 6, 7, 8],  # pz, tz, as, cz
foreground_name="prostate",
fill_incisions=True,
)
```

## Model registry (ClearML)

**Always use `LATEST_ANATOMY_MODEL_ID` from `viseg/__init__.py`** rather than hardcoding an ID. The constant is updated automatically by the training pipeline.

Current value (2026-04-27): `"71aec22bf3044253b83a935b33141356"`

To query ClearML directly:

```python
from clearml import Model

models = Model.query_models(
project_name="viseg",
tags=["production"],
only_published=False,
max_results=1,
)
latest_id = models[0].id
```

Or fetch by ID:

```python
from clearml import InputModel
m = InputModel(model_id=LATEST_ANATOMY_MODEL_ID)
local_path = m.get_local_copy()
```

The ClearML server address and credentials come from your local `~/.clearml.conf` — not from this page.

## Data access

For pulling imaging volumes from the vxData platform, see **`mono/packages/vxdata-sdk/README.md`**. The legacy `vxp_client` package was part of the archived `virdx/data-platform` repo (archived 2026-05-27); data access APIs are now in the vxData SDK under `mono/packages/vxdata-sdk`.

## What NOT to use

**Do not import from `viseg.study_inference`** — it has stale hardcoded model IDs and may fail with `virdx_core` version mismatches (`InstanceConnection` import error). Use `viseg.inference` directly.

Files to avoid:

- `viseg/study_inference/study_inference_params.py` — outdated model IDs
- `viseg/study_inference/study_anatomy_inference.py` — Study-based pipeline (stale)
- `viseg/study_inference/study_lesion_inference.py` — Study-based pipeline (stale)

## Vicom Volume I/O

```python
from vicom import Volume
from vicom.nifti import load_volume_from_nifti, write_to_nifti
from vicom.processing import resample_volume

vol = load_volume_from_nifti("/path/to/file.nii.gz")
write_to_nifti(vol, path="output.nii.gz", add_timestamp=False, store_json=True)

# Resample to match another volume's grid
resampled = resample_volume(vol, ref=reference_vol)
```

Key `Volume` attributes: `.data` (numpy), `.affine`, `.spacing`, `.volume_type`, `.b_value`, `.image_plane`, `.segmentation_labels`

## Pointers

- viseg source: `virdx/viseg/src/viseg/`
- Inference model: `virdx/viseg/src/viseg/inference/inference_model.py`
- Utils (binarize_mask): `virdx/viseg/src/viseg/inference/utils.py`
- Model constant: `virdx/viseg/src/viseg/__init__.py`
- vxData SDK: `mono/packages/vxdata-sdk/`

Source: https://docs.virdx.dev/knowledge/wiki/workstreams/histo/sops/viseg-inference/index.mdx