Models (dosma.models)
DOSMA currently supports pre-trained deep learning models for segmenting, each described in detail below.
Model aliases are string fields used to distinguish/specify particular models in DOSMA (command-line
argument --model).
All models are open-sourced under the GNU General Public License v3.0 license. If you use these models, please reference both DOSMA and the original work.
Model trained in Chaudhari et al. IWOAI 2018. |
|
Model trained by Team 6 in the 2019 IWOAI Segmentation Challenge. |
|
Extension of model trained by Team 6 in the 2019 IWOAI Segmentation Challenge (with normalization). |
|
Template for 2D U-Net models trained on the SKM-TEA dataset (previously 2021 Stanford qDESS Knee dataset). |
OAI 2D U-Net
A 2D U-Net trained on a downsampled rendition of the OAI iMorphics DESS dataset [CFLH18]. Inputs are zero-mean, unit standard deviation normalized before segmentation.
Aliases: oai-unet2d, oai_unet2d
IWOAI Segmentation Challenge - Team 6 2D U-Net
This model was submitted by Team 6 to the 2019 International Workshop on Osteoarthritis Segmentation [DCI+20]. It consists of a 2D U-Net trained on the standardized OAI training dataset.
Note, inputs are not normalized before segmentation and therefore may be difficult to generalize to DESS scans with different parameters than the OAI.
Aliases: iwoai-2019-t6
IWOAI Segmentation Challenge - Team 6 2D U-Net (Normalized)
This model is a duplicate of the iwoai-2019-t6 network (above), but differs in that it uses zero-mean, unit standard deviation normalized inputs. This may make the network more robust to different DESS scan parameters and/or scanner vendors.
While this model was not submitted to the IWOAI challenge, the architecture, training parameters, and dataset are identical to the Team 6 submission. Performance on the standardized OAI test set was similar to the original network submitted by Team 6 (see table below).
Aliases: iwoai-2019-t6-normalized
Femoral Cartilage |
Tibial Cartilage |
Patellar Cartilage |
Meniscus |
|
|---|---|---|---|---|
Dice |
0.906 +/- 0.014 |
0.881 +/- 0.033 |
0.857 +/- 0.080 |
0.870 +/- 0.032 |
VOE |
0.171 +/- 0.023 |
0.211 +/- 0.052 |
0.242 +/- 0.108 |
0.229 +/- 0.049 |
RMS-CV |
0.019 +/- 0.011 |
0.048 +/- 0.029 |
0.076 +/- 0.061 |
0.045 +/- 0.025 |
ASSD (mm) |
0.174 +/- 0.020 |
0.270 +/- 0.166 |
0.243 +/- 0.106 |
0.344 +/- 0.111 |
SKM-TEA qDESS Knee Segmentation - 2D U-net
This collection of models are trained on the SKM-TEA dataset (previously known as the 2021 Stanford qDESS Knee Dataset). Details of the different models that are trained are shown in the training configurations distributed with the weights.
qDESS_2021_v1-rms-unet2d-pc_fc_tc_men_weights.h5: This is the baseline RSS model trained on the SKM-TEA v1 dataset. Though the same hyperparameters were used, this model (trained with Tensorflow/Keras) performs better than the PyTorch implementation specified in the main paper. Results are shown in the table below.
qDESS_2021_v0_0_1-rms-pc_fc_tc_men_weights.h5: This model is trained on the 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).
Aliases: stanford-qdess-2021-unet2d, skm-tea-unet2d
Femoral Cartilage |
Tibial Cartilage |
Patellar Cartilage |
Meniscus |
|
|---|---|---|---|---|
Dice |
0.882 +/- 0.033 |
0.865 +/- 0.035 |
0.879 +/- 0.103 |
0.847 +/- 0.068 |
VOE |
0.210 +/- 0.052 |
0.237 +/- 0.053 |
0.205 +/- 0.121 |
0.261 +/- 0.092 |
CV |
0.051 +/- 0.033 |
0.053 +/- 0.037 |
0.049 +/- 0.077 |
0.052 +/- 0.052 |
ASSD (mm) |
0.265 +/- 0.114 |
0.354 +/- 0.250 |
0.477 +/- 0.720 |
0.485 +/- 0.307 |