---
license: apache-2.0
tags:
- medical-imaging
- segmentation
- image-segmentation
- ct
- lung
- nodule
- monai
- pytorch
library_name: pytorch
pipeline_tag: image-segmentation
---

# Lung Nodule Segmentation — SegResNet (3D, small)

Voxel-level segmentation of pulmonary nodules in 3D chest CT volumes,
cropped to the lung region. Small-capacity SegResNet
(`init_filters = 16`, ~20 M params) trained on lung-bbox crops from the
unified NLST + NSCLC + LIDC-IDRI corpus.

Named `segresnet_small` in the demo repo.

## Model details

- **Architecture**: [MONAI SegResNet](https://docs.monai.io/en/stable/networks.html#segresnet), 3D residual U-Net
- **Trainable parameters**: 20,663,538
- **Input**: `(1, 256, 256, 256)` CT crop, intensity-normalised to `[0, 1]`, resampled from a per-series lung bbox (padding = 20 vox)
- **Output**: `(2, 256, 256, 256)` softmax logits — class 0 = background, class 1 = nodule
- **Framework**: PyTorch + MONAI

## Data

Trained on the `unified` split (patient-grouped, dataset-stratified):

- NLST
- NSCLC-Radiomics
- LIDC-IDRI

Split sizes: **1 683 train / 297 val / 325 test (held out)**.
Nodule voxels are on the order of ~10⁻⁵ of the total — see the note on
metric interpretation below.

## Validation metrics

Evaluated on 297 val volumes (≈ 5 × 10⁹ voxels), micro-averaged at the
argmax of the 2-class softmax output.

| Metric              | Value    |
|---------------------|---------:|
| mIoU                | 0.7509   |
| Accuracy            | 0.9995   |
| Precision           | 0.6077   |
| Recall              | 0.7434   |
| Dice / F1 (micro)   | 0.6687   |
| IoU (nodule class)  | 0.5023   |
| Dice per case, mean | 0.5250   |
| Dice per case, med. | 0.5853   |

Because ~10⁻⁵ of voxels are nodule, `Accuracy` is trivially near 1.0
regardless of model quality and `mIoU` is dominated by `IoU_background`.
The operationally meaningful numbers are `Precision`, `Recall`, and the
per-case Dice distribution.

## How to load & run inference

```python
import yaml, torch
from monai.networks.nets import SegResNet

cfg = yaml.safe_load(open("config.yaml"))["model"]
model = SegResNet(
    spatial_dims = cfg["spatial_dims"],
    in_channels  = cfg["in_channels"],
    out_channels = cfg["out_channels"],
    init_filters = cfg["init_filters"],
    blocks_down  = tuple(cfg["blocks_down"]),
    blocks_up    = tuple(cfg["blocks_up"]),
    dropout_prob = cfg["dropout_prob"],
)
state = torch.load("model.pth", map_location="cpu", weights_only=True)
model.load_state_dict(state)
model.eval()

with torch.no_grad():
    # Input: (B, 1, 256, 256, 256), pre-cropped to the lung bbox and normalised to [0, 1]
    x = torch.randn(1, 1, 256, 256, 256)
    logits = model(x)                          # (B, 2, D, H, W)
    pred_class = logits.argmax(dim=1)          # (B, D, H, W)
    nodule_mask = (pred_class == 1).to(torch.uint8)
```

The model expects **lung-bbox-cropped** input. A separate 2D ROI model
is needed to produce that bbox — see the accompanying ROI checkpoints
(`roi-segresnet-2d` or `roi-swinunetr-2d`).

## Training recipe

- **Loss**: Focal Tversky + weighted CE (α=0.3, β=0.7, γ=2.0, λ_CE=0.1, nodule class weight = 100)
- **Optimizer**: Adam (lr = 1e-5, weight decay = 1e-5)
- **Scheduler**: CosineAnnealingLR (T_max = 400, η_min = 1e-6)
- **Batch size**: 4
- **Epochs**: 400  |  **best checkpoint at epoch 339 / 400**
- **Mixed precision**: bf16
- **Augmentation**: 3D flips, 90° rotations, elastic rotation, zoom, intensity scale/shift, Gaussian noise/blur, contrast
- **Seed**: 42
- **Hardware**: 1 × NVIDIA H100 94 GB
- **Wall-clock**: ≈ 3.5 days

Full config is included in this repo as `config.yaml`.

## Reproducing training

Training code lives in an accompanying reproduction demo (published
separately). Once available, reproduce with:

```bash
export DATA_ROOT=/path/to/unified          # dir containing ct_3d/ and nodule_sem_seg_3d/
python train.py --config config.yaml
```

## License & intended use

Model weights released under Apache 2.0. Training data was public but
covered by dataset-specific terms (NLST, NSCLC-Radiomics, LIDC-IDRI) —
users must comply with those separately when using the model on
comparable data.

**Not a medical device.** Not intended for clinical use. Research only.

## Citation

Paper in preparation.