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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.

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