--- a
+++ b/docs/source/models.rst
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+.. _seg_models:
+
+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 :code:`--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.
+
+.. automodule::
+   dosma.models
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+
+   dosma.models.OAIUnet2D
+   dosma.models.IWOAIOAIUnet2D
+   dosma.models.IWOAIOAIUnet2DNormalized
+   dosma.models.StanfordQDessUNet2D
+
+
+OAI 2D U-Net
+--------------------------------------------------------------------------------
+A 2D U-Net trained on a downsampled rendition of the OAI iMorphics DESS dataset :cite:`chaudhari2018open`.
+Inputs are zero-mean, unit standard deviation normalized before segmentation.
+
+Aliases: :code:`oai-unet2d`, :code:`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
+:cite:`desai2020international`.
+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: :code:`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: :code:`iwoai-2019-t6-normalized`
+
+.. table:: Average (standard deviation) performance summary on OAI test set.
+           Coefficient of variation is calculated as root-mean-square value.
+
+    =========  ===================  ==================  ====================  ===============
+    ..         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 <https://github.com/StanfordMIMI/skm-tea>`_
+(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: :code:`stanford-qdess-2021-unet2d`, :code:`skm-tea-unet2d`
+
+
+.. table:: Mean +/- standard deviation performance summary on SKM-TEA v1 dataset.
+
+   =========  ===================  ==================  ====================  ===============
+   ..         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
+   =========  ===================  ==================  ====================  ===============
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