Alveolar Canal Segmentation

All differences between models are statistically significant (Wilcoxon signed-rank, p<0.001) except AttentionUnet vs SwinUNETR (p=0.20, ns).

Repository Structure

abhijithkj3690/alveolar-canal-segmentation/
├── unet/
│   └── best_model.pth           # 3D UNet (MONAI)
├── attention_unet/
│   └── best_model.pth           # AttentionUnet (MONAI)
├── dynunet/
│   └── best_model.pth           # DynUNet (MONAI)
├── swinunetr/
│   └── best_model.pth           # SwinUNETR (MONAI)
├── segresnet/
│   └── best_model.pth           # SegResNet (MONAI)
└── nnunetv2/
    ├── plans.json
    ├── dataset.json
    ├── dataset_fingerprint.json
    └── fold_0/
        ├── checkpoint_best.pth
        └── checkpoint_final.pth

Usage

Installation

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
pip install monai nibabel scipy scikit-image tqdm
pip install huggingface_hub

Load and run inference (MONAI models)

import torch
from monai.networks.nets import AttentionUnet
from huggingface_hub import hf_hub_download

# Download weights
weights_path = hf_hub_download(
    repo_id="abhijithkj3690/alveolar-canal-segmentation",
    filename="attention_unet/best_model.pth",
)

# Build model — must match training architecture exactly
model = AttentionUnet(
    spatial_dims=3,
    in_channels=1,
    out_channels=1,
    channels=(32, 64, 128, 256, 320),
    strides=(2, 2, 2, 2),
    dropout=0.1,
)

model.load_state_dict(
    torch.load(weights_path, map_location="cpu", weights_only=True)
)
model.eval()

Available model strings

MONAI model constructors

from monai.networks.nets import UNet, AttentionUnet, DynUNet, SwinUNETR, SegResNet

# 3D UNet
UNet(spatial_dims=3, in_channels=1, out_channels=1,
     channels=(32,64,128,256,320), strides=(2,2,2,2), num_res_units=2)

# AttentionUnet
AttentionUnet(spatial_dims=3, in_channels=1, out_channels=1,
              channels=(32,64,128,256,320), strides=(2,2,2,2), dropout=0.1)

# DynUNet
DynUNet(spatial_dims=3, in_channels=1, out_channels=1,
        kernel_size=[[3,3,3]]*5,
        strides=[[1,1,1],[2,2,2],[2,2,2],[2,2,2],[2,2,2]],
        upsample_kernel_size=[[2,2,2]]*4,
        filters=(32,64,128,256,320), dropout=0.1,
        deep_supervision=False, res_block=True)

# SwinUNETR
SwinUNETR(in_channels=1, out_channels=1, feature_size=48,
          depths=(2,2,2,2), num_heads=(3,6,12,24), window_size=7,
          dropout_path_rate=0.1, spatial_dims=3)

# SegResNet
SegResNet(spatial_dims=3, in_channels=1, out_channels=1,
          init_filters=32, blocks_down=(1,2,2,4), blocks_up=(1,1,1),
          dropout_prob=0.1, use_conv_final=True)

Preprocessing

All MONAI models use the following preprocessing (must be applied before inference):

from monai.transforms import Compose, LoadImaged, EnsureChannelFirstd, Orientationd, \
    Spacingd, ScaleIntensityRanged, SpatialPadd, DivisiblePadd, EnsureTyped

transforms = Compose([
    LoadImaged(keys=["image"]),
    EnsureChannelFirstd(keys=["image"]),
    Orientationd(keys=["image"], axcodes="RAS", labels=None),
    Spacingd(keys=["image"], pixdim=(0.3, 0.3, 0.3), mode="bilinear"),
    ScaleIntensityRanged(keys=["image"],
        a_min=-167.8, a_max=1111.5, b_min=0.0, b_max=1.0, clip=True),
    SpatialPadd(keys=["image"], spatial_size=(128, 128, 128)),
    DivisiblePadd(keys=["image"], k=32),
    EnsureTyped(keys=["image"]),
])

Training Details

Code

Full training, inference, and analysis scripts: 👉 github.com/abhijithkj3690/alveolar-canal-segmentation

Dataset

ToothFairy2 Challenge — 3D CBCT scans with expert annotations of the alveolar canal (inferior alveolar nerve canal).

License

MIT — free to use for research and commercial purposes with attribution.

nnunetv2/fold_0/checkpoint_best.pth

pth · 235 MB · SHA-256 2cc806fbc3f9…1f70 · Hugging Face