publish model and README

LICENSE.md

@@ -0,0 +1,201 @@
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README.md

@@ -1,3 +1,42 @@
----
-license: agpl-3.0
----
+# EyeNED OCT-ONH: vendor-neutral analysis of optic nerve head OCT
+
+![EyeNED logo](resources/eyened-logo.png "EyeNED logo")
+
+This repository provides a vendor-neutral assessment of the anatomy of the optic nerve head, including the extraction of common features related to optic neuropathy, such as cpRNFL thickness and BMO-MRW. This method contains two steps:
+1. A CNN-based segmentation of the ONH anatomy -- which was implemented in [nnUNetv2](https://github.com/MIC-DKFZ/nnUNet/). 
+2. Comprehensive feature extraction, for both 3D cube or raster acquisitions and circular/radial scan patterns
+
+![Overview of biomarkers](resources/biomarker-overview.png "Overview of biomarkers")
+
+The model was trained on the following devices and acquisition modes:
+- Zeiss Cirrus HD-OCT 5000 -- 6x6mm cube (200x200)
+- Heidelberg Spectralis -- circular and radial scans
+- Topcon 3D OCT 2000 -- 6x6mm raster (128x512)
+
+## Using our model
+
+This repository contains the model weights and metadata. We used [nnunetv2](https://github.com/MIC-DKFZ/nnUNet) to train the model, so look into that repository to learn more about its implementation and usage.
+
+If you use this model for your research, please cite our manuscript:
+
+S.J. Driessen et al. CNN-based device-agnostic feature extraction from ONH OCT scans, Translational Vision Science & Technology (Accepted, 2024) -- details will be added upon publication
+
+Please feel free to post an issue if you run into them. Are you interested in the details or do you want to collaborate further? Please contact k.vangarderen \[at\] erasmusmc.nl
+
+## Copyright notice
+
+EyeNED OCT-ONH segmentation and feature extraction
+Copyright (C) 2024  Erasmus MC
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU Affero General Public License as published
+by the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU Affero General Public License for more details.
+
+You should have received a copy of the GNU Affero General Public License
+along with this program.  If not, see <https://www.gnu.org/licenses/>.

model/nnUNet_plans/dataset.json

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+{
+    "channel_names": {
+        "0": "OCT"
+    },
+    "labels": {
+        "background": 0,
+        "RNFL": 1,
+        "BM + PPA A/B": [
+            2,
+            5,
+            6
+        ],
+        "BM": 2,
+        "LC": 3,
+        "Vessel": 4,
+        "PPA A/B": [
+            5,
+            6
+        ],
+        "PPA - alpha": 5,
+        "PPA - beta": 6,
+        "PPA - gamma": 7
+    },
+    "regions_class_order": [
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9
+    ],
+    "numTraining": 1178,
+    "file_ending": ".png"
+}

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model/nnUNet_plans/plans.json

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+{
+    "dataset_name": "Dataset603_papil_corrected",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        999.0,
+        1.0,
+        1.0
+    ],
+    "original_median_shape_after_transp": [
+        1,
+        885,
+        512
+    ],
+    "image_reader_writer": "NaturalImage2DIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 13,
+            "patch_size": [
+                640,
+                384
+            ],
+            "median_image_size_in_voxels": [
+                885.0,
+                512.0
+            ],
+            "spacing": [
+                1.0,
+                1.0
+            ],
+            "normalization_schemes": [
+                "ZScoreNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
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+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                6,
+                6
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
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+                [
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+                    2
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+                [
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+                    2
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+                [
+                    2,
+                    2
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+                [
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 512,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": true
+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 255.0,
+            "mean": 112.79879760742188,
+            "median": 109.0,
+            "min": 0.0,
+            "percentile_00_5": 9.0,
+            "percentile_99_5": 246.0,
+            "std": 49.21487808227539
+        }
+    }
+}