dosma.models.StanfordQDessUNet2D
- class dosma.models.StanfordQDessUNet2D(input_shape, weights_path, force_weights=False)[source]
2D U-Net model trained on the 2021 Stanford qDESS Knee dataset.
This model segments patellar cartilage (“pc”), femoral cartilage (“fc”), tibial cartilage (“tc”), and the meniscus (“men”) from quantitative double echo steady state (qDESS) knee scans. The segmentation is computed on the root-sum-of-squares (RSS) of the two echoes.
There are a few weights files that are associated with this model. We provide a short description of each below:
qDESS_2021_v1-rms-unet2d-pc_fc_tc_men_weights.h5: This is the baselinemodel trained on the 2021 Stanford qDESS knee dataset (v1.0.0).
qDESS_2021_v0_0_1-rms-pc_fc_tc_men_weights.h5: This model is trained on the RSS2021 Stanford qDESS knee dataset (v0.0.1).
qDESS_2021_v0_0_1-traintest-rms-pc_fc_tc_men_weights.h5: This modelis trained on both the train and test set of the 2021 Stanford qDESS knee dataset (v0.0.1).
Examples
# Create model based on the volume’s shape (SI, AP, 1). >>> model = StanfordQDessUNet2D((256, 256, 1), “/path/to/weights”)
# Generate mask from root-sum-of-squares (rss) volume. >>> model.generate_mask(rss)
# Generate mask from dual-echo volume de_vol - shape: (SI, AP, LR, 2) >>> model.generate_mask(de_vol)
- __init__(input_shape, weights_path, force_weights=False)
Methods
__init__(input_shape, weights_path[, ...])build_model(input_shape[, weights_path])Builds a segmentation model architecture and loads weights.
generate_mask(volume)Segment tissues.
Attributes
ALIASESsigmoid_threshold