dosma.models.StanfordQDessUNet2D
- class dosma.models.StanfordQDessUNet2D(input_shape, weights_path, force_weights=False)[source]
Template for 2D U-Net models trained on the SKM-TEA dataset (previously 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 baseline model trained on the SKM-TEA dataset (v1.0.0).qDESS_2021_v0_0_1-rms-pc_fc_tc_men_weights.h5: This model is trained on the RSS 2021 Stanford qDESS knee dataset (v0.0.1).qDESS_2021_v0_0_1-traintest-rms-pc_fc_tc_men_weights.h5: This model is 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