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license: apache-2.0
tags:
- medical-imaging
- segmentation
- image-segmentation
- ct
- lung
- monai
- pytorch
library_name: pytorch
pipeline_tag: image-segmentation
---
# Lung ROI Segmentation — SegResNet (2D)
Per-slice 2D lung foreground (thoracic parenchyma) segmentation for
chest CT. Used as the ROI stage of a two-stage coarse-to-fine
pulmonary-nodule segmentation pipeline: its outputs are stacked into a
3D lung bounding box that the downstream nodule model crops to.
## Model details
- **Architecture**: [MONAI SegResNet](https://docs.monai.io/en/stable/networks.html#segresnet), 2D residual U-Net
- **Trainable parameters**: 6,904,081
- **Input**: `(1, 256, 256)` axial CT slice, intensity-normalised to `[0, 1]`
- **Output**: `(1, 256, 256)` sigmoid map; foreground = lung tissue
- **Framework**: PyTorch + MONAI
## Data
Trained on the `unified` split (patient-grouped, dataset-stratified)
of a unified corpus assembled from three public sources:
- NLST
- NSCLC-Radiomics
- LIDC-IDRI
Slices per split: **365 014 train / 64 097 val / — test held out**.
## Validation metrics
Evaluated on 64 097 val slices (≈ 4.2 × 10⁹ pixels), micro-averaged at
0.5 threshold on sigmoid output.
| Metric | Value |
|-------------|-------:|
| mIoU | 0.9803 |
| Accuracy | 0.9953 |
| Precision | 0.9797 |
| Recall | 0.9857 |
| Dice (F1) | 0.9827 |
## 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():
x = torch.randn(1, 1, 256, 256) # (B, C, H, W) — replace with your CT slice
prob = torch.sigmoid(model(x))
lung_mask = (prob > 0.5).to(torch.uint8)
```
## Training recipe
- **Loss**: `DiceLoss(sigmoid=True, squared_pred=True)`
- **Optimizer**: Adam (lr = 1e-3, weight decay = 1e-5)
- **Scheduler**: CosineAnnealingLR (T_max = 100, η_min = 1e-6)
- **Batch size**: 16
- **Samples/epoch**: 20 000 (random subsample of ~365 k train slices)
- **Epochs budget**: 100 | **best checkpoint at epoch 7 / 100**
- **Mixed precision**: bf16
- **Seed**: 42
- **Hardware**: 1 × NVIDIA H100 94 GB
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_2d/ and roi_sem_seg_2d/
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.
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