metadata
license: other
license_name: deepcan-research-license
license_link: LICENSE
library_name: pytorch
tags:
- medical-imaging
- segmentation
- brain-mri
- veterinary
- canine
- 3d-unet
- position-encoding
language:
- en
pipeline_tag: image-segmentation
DeepCAN-SEG-PosEnc: Canine Brain MRI Segmentation Model
GitHub: https://github.com/Core-BMC/DeepCAN-SegSR.git
A lightweight canine brain MRI segmentation model with 3D position encoding, targeting 9 essential brain anatomical classes.
Model Description
This model performs multi-class brain parcellation on canine MRI scans, segmenting 9 anatomical regions with left/right hemisphere discrimination.
- Architecture: 3D UNet with residual blocks (LRSegmentationMultiClassUNet)
- Input: 64x64x64 patches with 4 channels (intensity + xyz position encoding)
- Output: 9-class segmentation mask
- Parameters: ~66MB
Segmentation Classes
| ID | Left Hemisphere | ID | Right Hemisphere |
|---|---|---|---|
| 1 | Ventricles (Left) | 5 | Ventricles (Right) |
| 2 | Gray Matter (Left) | 6 | Gray Matter (Right) |
| 3 | White Matter (Left) | 7 | White Matter (Right) |
| 4 | Cerebellum (Left) | 8 | Cerebellum (Right) |
| 0 | Background |
Performance
Validation Metrics (Epoch 26)
| Left Hemisphere | Dice | Right Hemisphere | Dice |
|---|---|---|---|
| Ventricle_L | 0.9046 | Ventricle_R | 0.8998 |
| Gray Matter_L | 0.9123 | Gray Matter_R | 0.9036 |
| White Matter_L | 0.8581 | White Matter_R | 0.8604 |
| Cerebellum_L | 0.9558 | Cerebellum_R | 0.9489 |
| Background | 0.9954 |
Mean Validation Dice: 0.9054
Mean Validation Loss: 1.019
Training Metrics
- Train Dice: 0.946
- Train Loss: 0.060
Training Details
- Dataset: DeepCAN v1.1a (balanced L/R patches, remapped labels)
- Epochs: 26 (early stopped, patience 20)
- Batch Size: 24
- Learning Rate: 1e-4 (cosine scheduler, T_max=500, eta_min=1e-6)
- Optimizer: AdamW (weight_decay=1e-5)
- Loss: MultiClass Dice + Cross-Entropy (dice_weight=0.7, gradual class weights)
- Gradient Accumulation: 4 steps
- Hardware: NVIDIA RTX 4090 (24GB)
- Training Time: ~23.7 hours
Training Logs
Full training logs available on Weights & Biases:
- Project: DeepCAN-SegSR
- Run:
DeepCAN-SEG-PosEnc-v11a
Usage
import torch
from src.models.lr_segmentation_model import LRSegmentationMultiClassUNet
# Load model
model = LRSegmentationMultiClassUNet(
in_channels=4, # intensity + xyz position encoding
num_classes=9,
features=[32, 64, 128, 256]
)
checkpoint = torch.load("DeepCAN-SEG-PosEnc.pth", map_location="cpu")
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()
# Inference
with torch.no_grad():
# input_patch: [B, 4, 64, 64, 64] - intensity + normalized xyz coords
output = model(input_patch)
prediction = torch.argmax(output, dim=1)
With Clinical Pipeline
# Clone the main repository
git clone https://github.com/Core-BMC/DeepCAN-SegSR.git
cd DeepCAN-SegSR
# Run clinical pipeline
python -m src.inference.cli clinical \
--input your_dicom_folder/ \
--output outputs/
Model Files
DeepCAN-SEG-PosEnc.pth: Model weights (66MB)
Limitations
- Trained on canine brain MRI only (not validated for other species)
- Optimized for T2-weighted sequences
- Requires preprocessing to match training data distribution
- Research use only - not validated for clinical diagnosis
Citation
@software{deepcan2025,
title = {DeepCAN SegSR Suite: Canine Brain MRI Super-Resolution and Segmentation},
author = {Hwon Heo & Woo Hyun Shim, DeepCAN AI team},
year = {2025},
url = {https://github.com/Core-BMC/DeepCAN-SegSR}
}
License
This model is released under the DeepCAN Research License - free for non-commercial research and educational use only.
For commercial licensing inquiries, contact: heohwon@gmail.com
See LICENSE for full terms.