MedianNerveDL

A record code for "Automated segmentation of median nerve in dynamic sonography using deep learning: Evaluation of model performance".

Current codes are only for inference.

Requirements

python environment

• PyTorch 1.1 or newer version. (for U-Net, FPN, MaskRCNN)
• torchvision 0.3.0 or newer version (for U-Net, FPN, MaskRCNN)
• tensorflow 1.9.0 or newer version (for Deeplabv3+)
  • tensorflow 2.X is not supported
• opencv
• pillow
• matplotlib
• pandas
• numpy

Ground truth map

Ground truth should follow below polcy.

• Naming: <input_file_name>_mask.png
• Format: png
• A binary map with 1 for Median Nerve pixels, and 0 for background pixels.

Model

Model_zoo will be further updated.

U-Net

Weights

• u-net_resnet-101
• u-net_resnext101-32x8d

Command

With ground truth

python ./inference_deeplab_option.py --predict_dir <folder_contains_input_images> --model_type unet --backbone <resnet101/resnext101_32x8d> --output_dir <folder_for_output> --gt_dir <folder_contains_ground_truth_masks> --model_path <weights_file>

Without ground truth

python ./inference_option_withoutgt.py --predict_dir <folder_contains_input_images> --model_type unet --backbone <resnet101/resnext101_32x8d> --output_dir <folder_for_output> --model_path <weights_file>

FPN

Weights

• FPN_resnet-101
• FPN_resnext101-32x8d

Command

With ground truth

python ./inference_deeplab_option.py --predict_dir <folder_contains_input_images> --model_type fpn --backbone <resnet101/resnext101_32x8d> --output_dir <folder_for_output> --gt_dir <folder_contains_ground_truth_masks> --model_path <weights_file>

Without ground truth

python ./inference_option_withoutgt.py --predict_dir <folder_contains_input_images> --model_type fpn --backbone <resnet101/resnext101_32x8d> --output_dir <folder_for_output> --model_path <weights_file>

Deeplabv3+

Deeplabv3+ was trained with Deeplab project

Weights

• Deeplabv3+_xception-65
• Deeplabv3+_resnet-101

Command

With ground truth

python ./inference_deeplab.py --predict_dir <folder_contains_input_images> --output_dir <folder_for_output> --gt_dir <folder_contains_ground_truth_masks> --model_path <frozen_graph_file>

Without ground truth

python ./inference_deeplab_withoutgt.py --predict_dir <folder_contains_input_images> --output_dir <folder_for_output> --model_path <frozen_graph_file>

MaskRCNN

For Mask R-CNN, we use maskrcnn-benchmark