complete the model package

LICENSE

@@ -0,0 +1,201 @@
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+   Copyright [2023] [Chernenkiy Ivan Michailovich]
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README.md

@@ -0,0 +1,141 @@
+---
+tags:
+- monai
+- medical
+library_name: monai
+license: apache-2.0
+---
+# Model Title
+Renal structures CECT segmentation
+
+### **Authors**
+Ivan Chernenkiy,   Michael Chernenkiy,   Dmitry Fiev,   Evgeny Sirota, Center for Neural Network Technologies / Institute of Urology and Human Reproductive Systems / Sechenov First Moscow State Medical University
+
+### **Tags**
+Segmentation, CT, CECT, Kidney, Renal, Supervised
+
+## **Model Description**
+The model is the SegResNet architecture[1] for volumetric (3D) renal structures segmentation. Input is artery, vein, excretory phases after mutual registration and concatenated to 3 channel 3D tensor.
+
+
+## **Data**
+DICOM data from 41 patients with kidney neoplasms were used [2]. The images and segmentation data are available under a CC BY-NC-SA 4.0 license. Data included all phases of contrast-enhanced multispiral computed tomography. We split the data: 32 observations for the training set and 9 – for the validation set. At the labeling stage, the arterial, venous, and excretory phases were taken, affine registration was performed to jointly match the location of the kidneys, and noise was removed using a median filter and a non-local means filter. Validation set ip published to Yandex.Disk. You can download via [link](https://disk.yandex.ru/d/pWEKt6D3qi3-aw) or use following command:
+```bash
+python -m monai.bundle run download_data --meta_file configs/metadata.json --config_file "['configs/train.json', 'configs/evaluate.json']"
+```
+
+**NB**: underlying data is in LPS orientation. IF! you want to test model on your own data, reorient it from RAS to LPS with `Orientation` transform. You can see example of preprocessing pipeline in `inference.json` file of this bundle.
+
+#### **Preprocessing**
+Images are (1) croped to kidney region, all (artery,vein,excret) phases are (2) [registered](https://simpleitk.readthedocs.io/en/master/registrationOverview.html#lbl-registration-overview) with affine transform, noise removed with (3) median and (4) non-local means filter. After that, images are (5) resampled to (0.8,0.8,0.8) density and intesities are (6) scaled from [-1000,1000] to [0,1] range.
+
+## **Performance**
+On the validation subset, the values of the Dice score of the SegResNet architecture were: 0.89 for the normal parenchyma of the kidney, 0.58 for the kidney neoplasms, 0.86 for arteries, 0.80 for veins, 0.80 for ureters.
+
+When compared with the nnU-Net model, which was trained on KiTS 21 dataset, the Dice score was greater for the kidney parenchyma in SegResNet – 0.89 compared to three model variants: lowres – 0.69, fullres – 0.70, cascade – 0.69. At the same time, for the neoplasms of the parenchyma of the kidney, the Dice score was comparable: for SegResNet – 0.58, for nnU-Net fullres – 0.59; lowres and cascade had lower Dice score of 0.37 and 0.45, respectively. To reproduce, visit - https://github.com/blacky-i/nephro-segmentation
+
+
+## **Additional Usage Steps**
+
+#### Execute training:
+
+```bash
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json
+```
+Expected result: finished, Training process started
+
+
+#### Execute training with finetuning
+```bash
+python -m monai.bundle run training --dont_finetune false --meta_file configs/metadata.json --config_file configs/train.json
+```
+Expected result: finished, Training process started, model variables are restored
+
+#### Execute validation:
+
+Download validation data (described in [Data](#data) section).
+
+With provided model weights mean dice score is expected to be ~0.78446.
+
+#####  Run validation script:
+```bash
+python -m monai.bundle run evaluate --meta_file configs/metadata.json --config_file "['configs/train.json', 'configs/evaluate.json']"
+```
+Expected result: finished, `Key metric: val_mean_dice best value: ...` is printed.
+
+## **System Configuration**
+The model was trained for 10000 epochs on 2 RTX2080Ti GPUs with [SmartCacheDataset](https://docs.monai.io/en/stable/data.html#smartcachedataset). This takes 1 days and 2 hours, with 4 images per GPU.
+Training progress is available on [tensorboard.dev](https://tensorboard.dev/experiment/VlEMjLdURH6SyFp216dFBg)
+
+To perform training in minimal settings, at least one 12GB-memory GPU is required.
+Actual Model Input: 96 x 96 x 96
+
+## **Limitations**
+For developmental purposes only and cannot be used directly for clinical procedures.
+
+## **Citation Info**
+```
+@article{chernenkiy2023segmentation,
+  title={Segmentation of renal structures based on contrast computed tomography scans using a convolutional neural network},
+  author={Chernenkiy, IМ and Chernenkiy, MM and Fiev, DN and Sirota, ES},
+  journal={Sechenov Medical Journal},
+  volume={14},
+  number={1},
+  pages={39--49},
+  year={2023}
+}
+```
+
+## **References**
+
+[1] Myronenko, A. (2019). 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science(), vol 11384. Springer, Cham. https://doi.org/10.1007/978-3-030-11726-9_28
+
+[2] Chernenkiy, I. М., et al. "Segmentation of renal structures based on contrast computed tomography scans using a convolutional neural network." Sechenov Medical Journal 14.1 (2023): 39-49.https://doi.org/10.47093/2218-7332.2023.14.1.39-49
+
+#### **Tests used for bundle checking**
+
+Checking with ci script file
+```bash
+python ci/verify_bundle.py -b renalStructures_CECT_segmentation -p models
+```
+Expected result: passed, model.pt file downloaded
+
+
+Checking downloading validation data file
+```bash
+cd models/renalStructures_CECT_segmentation
+python -m monai.bundle run download_data --meta_file configs/metadata.json --config_file "['configs/train.json', 'configs/evaluate.json']"
+```
+Expected result: finished, `data/` folder is created and filled with images.
+
+
+Checking evaluation script
+```bash
+python -m monai.bundle run evaluate --meta_file configs/metadata.json --config_file "['configs/train.json', 'configs/evaluate.json']"
+```
+Expected result: finished, `Key metric: val_mean_dice best value: ...` is printed.
+
+
+Checking train script
+```bash
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json
+```
+Expected result: finished, Training process started
+
+
+Checking train script with finetuning
+```bash
+python -m monai.bundle run training --dont_finetune false --meta_file configs/metadata.json --config_file configs/train.json
+```
+Expected result: finished, Training process started, model variables are restored
+
+Checking inference script
+```bash
+python -m monai.bundle run inference --meta_file configs/metadata.json --config_file configs/inference.json
+```
+Expected result: finished, in `eval` folder masks are created
+
+Check unit test with script:
+```bash
+python ci/unit_tests/runner.py --b renalStructures_CECT_segmentation
+```

configs/evaluate.json

@@ -0,0 +1,56 @@
+{
+    "imports": [
+        "$import glob",
+        "$import os",
+        "$import ignite",
+        "$import json",
+        "$import urllib"
+    ],
+    "validate#handlers": [
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        },
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@ckpt_dir + '/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "MetricsSaver",
+            "save_dir": "@output_dir",
+            "metrics": [
+                "val_mean_dice",
+                "ar/dice",
+                "ve/dice",
+                "ur/dice",
+                "ki/dice",
+                "tu/dice",
+                "tu/haunsdorff",
+                "tu/surface"
+            ],
+            "metric_details": [
+                "val_mean_dice"
+            ],
+            "batch_transform": "$monai.handlers.from_engine(['image_meta_dict'])",
+            "summary_ops": "*"
+        }
+    ],
+    "download_data": [
+        "$import logging",
+        "$import zipfile",
+        "$import os",
+        "$logging.info('Downloading 298Mb data zip archive, please wait... (To see progress bar, download manually from https://disk.yandex.ru/d/pWEKt6D3qi3-aw , and extract data to bundle_root - ' + @bundle_root + ')')",
+        "$urllib.request.urlretrieve(json.loads(urllib.request.urlopen('https://cloud-api.yandex.net/v1/disk/public/resources/download?public_key=https%3A%2F%2Fdisk.yandex.ru%2Fd%2FpWEKt6D3qi3-aw').read())['href'], @bundle_root + '/AVUCTK_cases.zip')",
+        "$zipfile.ZipFile(os.path.join(@bundle_root, 'AVUCTK_cases.zip'), 'r').extractall(@bundle_root)",
+        "$os.remove(os.path.join(@bundle_root, 'AVUCTK_cases.zip'))",
+        "$logging.info('Data extracted to ' +  @bundle_root)"
+    ],
+    "evaluate": [
+        "$monai.utils.set_determinism(seed=42)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@validate#evaluator.run()"
+    ]
+}

configs/inference.json

@@ -0,0 +1,194 @@
+{
+    "imports": [
+        "$import glob",
+        "$import os"
+    ],
+    "bundle_root": ".",
+    "ckpt_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "$@bundle_root + '/data'",
+    "images": "$[{'artery':a, 'vein':b, 'excret':c }for a,b,c in  zip(glob.glob(@dataset_dir + '/*/12.nii.gz'), glob.glob(@dataset_dir + '/*/22-.nii.gz'), glob.glob(@dataset_dir + '/*/32-.nii.gz'))]",
+    "labels": "$list(glob.glob(@dataset_dir + '/*/merged.nii.gz'))",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "SegResNet",
+        "in_channels": 3,
+        "out_channels": 6,
+        "init_filters": 32,
+        "upsample_mode": "deconv",
+        "dropout_prob": 0.2,
+        "norm_name": "group",
+        "blocks_down": [
+            1,
+            2,
+            2,
+            4
+        ],
+        "blocks_up": [
+            1,
+            1,
+            1
+        ]
+    },
+    "network": "$@network_def.to(@device)",
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": [
+                    "artery",
+                    "vein",
+                    "excret",
+                    "label"
+                ]
+            },
+            {
+                "_target_": "EnsureChannelFirstd",
+                "keys": [
+                    "artery",
+                    "vein",
+                    "excret"
+                ]
+            },
+            {
+                "_target_": "Orientationd",
+                "keys": [
+                    "artery",
+                    "vein",
+                    "excret"
+                ],
+                "axcodes": "LPS"
+            },
+            {
+                "_target_": "Spacingd",
+                "keys": [
+                    "artery",
+                    "vein",
+                    "excret"
+                ],
+                "pixdim": [
+                    0.8,
+                    0.8,
+                    0.8
+                ],
+                "mode": "bilinear"
+            },
+            {
+                "_target_": "scripts.my_transforms.ConcatImages",
+                "keys_merge": [
+                    "artery",
+                    "vein",
+                    "excret"
+                ],
+                "keys_out": "image"
+            },
+            {
+                "_target_": "ScaleIntensityRanged",
+                "keys": "image",
+                "a_min": -1000,
+                "a_max": 1000,
+                "b_min": 0.0,
+                "b_max": 1.0,
+                "clip": true
+            },
+            {
+                "_target_": "EnsureTyped",
+                "keys": "image"
+            }
+        ]
+    },
+    "dataset": {
+        "_target_": "Dataset",
+        "data": "$[{'label': l, **i} for i, l in zip(@images, @labels)]",
+        "transform": "@preprocessing"
+    },
+    "dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@dataset",
+        "batch_size": 1,
+        "shuffle": false,
+        "num_workers": 4
+    },
+    "inferer": {
+        "_target_": "SlidingWindowInferer",
+        "roi_size": [
+            96,
+            96,
+            96
+        ],
+        "sw_batch_size": 4,
+        "overlap": 0.25
+    },
+    "postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Invertd",
+                "transform": "$@preprocessing",
+                "device": "@device",
+                "keys": "pred",
+                "orig_keys": "artery",
+                "meta_keys": "pred_meta_dict",
+                "nearest_interp": false,
+                "to_tensor": true
+            },
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "softmax": false,
+                "sigmoid": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": "pred",
+                "threshold": 0.5
+            },
+            {
+                "_target_": "scripts.my_transforms.MergeClassesd",
+                "keys": "pred"
+            },
+            {
+                "_target_": "SaveImaged",
+                "keys": "pred",
+                "meta_keys": "pred_meta_dict",
+                "data_root_dir": "@dataset_dir",
+                "output_dir": "@output_dir"
+            },
+            {
+                "_target_": "SaveImaged",
+                "keys": "label",
+                "data_root_dir": "@dataset_dir",
+                "output_dir": "@output_dir"
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@ckpt_dir + '/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            },
+            "strict": "True"
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        }
+    ],
+    "evaluator": {
+        "_target_": "SupervisedEvaluator",
+        "device": "@device",
+        "val_data_loader": "@dataloader",
+        "network": "@network",
+        "inferer": "@inferer",
+        "postprocessing": "@postprocessing",
+        "val_handlers": "@handlers",
+        "amp": false
+    },
+    "inference": [
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@evaluator.run()"
+    ]
+}

configs/logging.conf

@@ -0,0 +1,21 @@
+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=fullFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=fullFormatter
+args=(sys.stdout,)
+
+[formatter_fullFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s

configs/metadata.json

@@ -0,0 +1,84 @@
+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
+    "version": "0.1.0",
+    "changelog": {
+        "0.1.0": "complete the model package"
+    },
+    "monai_version": "1.2.0",
+    "pytorch_version": "1.13.1",
+    "numpy_version": "1.24.3",
+    "optional_packages_version": {
+        "nibabel": "5.1.0",
+        "pytorch-ignite": "0.4.11",
+        "einops": "0.6.1",
+        "fire": "0.5.0",
+        "torchvision": "0.14.1"
+    },
+    "name": "Segmentation of renal structures based on contrast computed tomography scans",
+    "task": "Renal structures segmentation",
+    "description": "A UNET-based model for renal segmentation from contrast enhanced CT image",
+    "authors": "Sechenov university",
+    "copyright": "Copyright (c) Sechenov university",
+    "data_source": "AVUCTK_cases.zip",
+    "data_type": "nibabel",
+    "image_classes": "three channel data, intensity scaled to [0, 1]",
+    "label_classes": "1: artery, 2: vein, 3: ureter, 4: cyst, 5: tumor, 6: parenchyma",
+    "pred_classes": "1: artery, 2: vein, 3: ureter, 4: neoplasm, 5: parenchyma",
+    "eval_metrics": {
+        "mean_dice": 0.79
+    },
+    "intended_use": "This is  PoC, not to be used for diagnostic purposes",
+    "references": [
+        "Chernenkiy I. M. et al. Segmentation of renal structures based on contrast computed tomography scans using a convolutional neural network //Sechenov Medical Journal. \u2013 2023. \u2013 \u0422. 14. \u2013 \u2116. 1. \u2013 \u0421. 39-49. URL - https://www.sechenovmedj.com/jour/article/view/899"
+    ],
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "image",
+                "format": "hounsfield",
+                "modality": "CT",
+                "num_channels": 3,
+                "spatial_shape": [
+                    96,
+                    96,
+                    96
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": true,
+                "channel_def": {
+                    "0": "image"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "segmentation",
+                "num_channels": 6,
+                "spatial_shape": [
+                    96,
+                    96,
+                    96
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": true,
+                "channel_def": {
+                    "0": "background",
+                    "1": "artery",
+                    "2": "vein",
+                    "3": "ureter",
+                    "4": "neoplasm",
+                    "5": "parenchyma"
+                }
+            }
+        }
+    }
+}

configs/train.json

@@ -0,0 +1,500 @@
+{
+    "imports": [
+        "$import glob",
+        "$import os",
+        "$import ignite"
+    ],
+    "bundle_root": ".",
+    "ckpt_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "$@bundle_root + '/data'",
+    "images": "$[{'artery':a, 'vein':b, 'excret':c }for a,b,c in  zip(glob.glob(@dataset_dir + '/*/12.nii.gz'), glob.glob(@dataset_dir + '/*/22-.nii.gz'), glob.glob(@dataset_dir + '/*/32-.nii.gz'))]",
+    "labels": "$list(glob.glob(@dataset_dir + '/*/merged.nii.gz'))",
+    "val_interval": 50,
+    "dont_finetune": true,
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "SegResNet",
+        "in_channels": 3,
+        "out_channels": 6,
+        "init_filters": 32,
+        "upsample_mode": "deconv",
+        "dropout_prob": 0.2,
+        "norm_name": "group",
+        "blocks_down": [
+            1,
+            2,
+            2,
+            4
+        ],
+        "blocks_up": [
+            1,
+            1,
+            1
+        ]
+    },
+    "network": "$@network_def.to(@device)",
+    "loss": {
+        "_target_": "DiceLoss",
+        "include_background": false,
+        "to_onehot_y": true,
+        "sigmoid": true,
+        "softmax": false,
+        "squared_pred": false,
+        "jaccard": false,
+        "reduction": "mean",
+        "smooth_nr": 0.0,
+        "smooth_dr": 1e-05,
+        "batch": false
+    },
+    "optimizer": {
+        "_target_": "Novograd",
+        "params": "$@network.parameters()",
+        "lr": 0.001,
+        "betas": [
+            0.9,
+            0.98
+        ],
+        "eps": 1e-08,
+        "weight_decay": 0,
+        "grad_averaging": false,
+        "amsgrad": false
+    },
+    "train": {
+        "deterministic_transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": [
+                    "artery",
+                    "vein",
+                    "excret",
+                    "label"
+                ],
+                "reader": null,
+                "overwriting": false,
+                "dtype": "float32",
+                "as_closest_canonical": true
+            },
+            {
+                "_target_": "EnsureChannelFirstd",
+                "keys": [
+                    "artery",
+                    "vein",
+                    "excret",
+                    "label"
+                ]
+            },
+            {
+                "_target_": "MapLabelValued",
+                "keys": "label",
+                "orig_labels": [
+                    0,
+                    1,
+                    2,
+                    3,
+                    4,
+                    5,
+                    6
+                ],
+                "target_labels": [
+                    0,
+                    1,
+                    2,
+                    3,
+                    4,
+                    4,
+                    5
+                ]
+            },
+            {
+                "_target_": "ToTensord",
+                "keys": [
+                    "artery",
+                    "vein",
+                    "excret",
+                    "label"
+                ]
+            },
+            {
+                "_target_": "Spacingd",
+                "keys": [
+                    "artery",
+                    "vein",
+                    "excret",
+                    "label"
+                ],
+                "pixdim": [
+                    0.8,
+                    0.8,
+                    0.8
+                ],
+                "mode": [
+                    "bilinear",
+                    "bilinear",
+                    "bilinear",
+                    "nearest"
+                ]
+            },
+            {
+                "_target_": "ScaleIntensityRanged",
+                "keys": [
+                    "artery",
+                    "vein",
+                    "excret"
+                ],
+                "a_min": -1000,
+                "a_max": 1000,
+                "b_min": 0.0,
+                "b_max": 1.0,
+                "clip": true
+            },
+            {
+                "_target_": "scripts.my_transforms.ConcatImages",
+                "keys_merge": [
+                    "artery",
+                    "vein",
+                    "excret"
+                ],
+                "keys_out": "image"
+            },
+            {
+                "_target_": "ToTensord",
+                "keys": [
+                    "image"
+                ]
+            }
+        ],
+        "random_transforms": [
+            {
+                "_target_": "RandZoomd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "prob": 0.3
+            },
+            {
+                "_target_": "RandAxisFlipd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "prob": 0.3
+            },
+            {
+                "_target_": "RandRotate90d",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "prob": 0.3
+            },
+            {
+                "_target_": "RandAdjustContrastd",
+                "keys": [
+                    "image"
+                ],
+                "prob": 0.5
+            },
+            {
+                "_target_": "RandHistogramShiftd",
+                "keys": "image",
+                "num_control_points": 10,
+                "prob": 0.3
+            },
+            {
+                "_target_": "DivisiblePadd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "k": 32
+            },
+            {
+                "_target_": "RandCropByLabelClassesd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "label_key": "label",
+                "num_classes": 6,
+                "spatial_size": [
+                    96,
+                    96,
+                    96
+                ],
+                "ratios": [
+                    1,
+                    2,
+                    2,
+                    3,
+                    3,
+                    1
+                ],
+                "num_samples": 4
+            }
+        ],
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": "$@train#deterministic_transforms + @train#random_transforms"
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "$[{'label': l, **i} for i, l in zip(@images, @labels)]",
+            "transform": "@train#preprocessing",
+            "cache_rate": 1.0,
+            "num_workers": 4
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@train#dataset",
+            "batch_size": 1,
+            "shuffle": true,
+            "num_workers": 2
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "postprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "Activationsd",
+                    "keys": "pred",
+                    "softmax": false,
+                    "sigmoid": true
+                },
+                {
+                    "_target_": "AsDiscreted",
+                    "keys": [
+                        "pred",
+                        "label"
+                    ],
+                    "argmax": [
+                        false,
+                        false
+                    ],
+                    "to_onehot": [
+                        null,
+                        6
+                    ],
+                    "threshold": [
+                        0.5,
+                        null
+                    ]
+                },
+                {
+                    "_target_": "SplitChanneld",
+                    "keys": [
+                        "pred",
+                        "label"
+                    ],
+                    "output_postfixes": [
+                        "bck",
+                        "ar",
+                        "ve",
+                        "ur",
+                        "tu",
+                        "ki"
+                    ]
+                }
+            ]
+        },
+        "handlers": [
+            {
+                "_target_": "CheckpointLoader",
+                "_disabled_": "@dont_finetune",
+                "load_path": "$@ckpt_dir + '/model.pt'",
+                "load_dict": {
+                    "model": "@network"
+                }
+            },
+            {
+                "_target_": "ValidationHandler",
+                "validator": "@validate#evaluator",
+                "epoch_level": true,
+                "interval": "@val_interval"
+            },
+            {
+                "_target_": "StatsHandler",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            }
+        ],
+        "key_metric": {
+            "train/mean_dice": {
+                "_target_": "MeanDice",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "additional_metrics": {
+            "train/tu_dice": {
+                "_target_": "MeanDice",
+                "include_background": true,
+                "output_transform": "$monai.handlers.from_engine(['pred_tu', 'label_tu'])"
+            }
+        },
+        "trainer": {
+            "_target_": "SupervisedTrainer",
+            "max_epochs": 10000,
+            "device": "@device",
+            "train_data_loader": "@train#dataloader",
+            "network": "@network",
+            "loss_function": "@loss",
+            "optimizer": "@optimizer",
+            "inferer": "@train#inferer",
+            "postprocessing": "@train#postprocessing",
+            "key_train_metric": "@train#key_metric",
+            "train_handlers": "@train#handlers",
+            "additional_metrics": "@train#additional_metrics",
+            "amp": true
+        }
+    },
+    "validate": {
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": "$@train#deterministic_transforms"
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "$[{'label': l, **i} for i, l in zip(@images, @labels)]",
+            "transform": "@validate#preprocessing",
+            "cache_rate": 1.0
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@validate#dataset",
+            "batch_size": 1,
+            "shuffle": false,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SlidingWindowInferer",
+            "roi_size": [
+                96,
+                96,
+                96
+            ],
+            "sw_batch_size": 4,
+            "overlap": 0.25
+        },
+        "postprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "Invertd",
+                    "transform": "%validate#preprocessing",
+                    "device": "@device",
+                    "keys": [
+                        "pred",
+                        "label"
+                    ],
+                    "orig_keys": [
+                        "artery",
+                        "label"
+                    ],
+                    "meta_keys": [
+                        "pred_meta_dict",
+                        "label_meta_dict"
+                    ],
+                    "nearest_interp": [
+                        false,
+                        true
+                    ],
+                    "to_tensor": true
+                },
+                "%train#postprocessing#transforms#0",
+                "%train#postprocessing#transforms#1",
+                "%train#postprocessing#transforms#2"
+            ]
+        },
+        "handlers": [
+            {
+                "_target_": "StatsHandler",
+                "iteration_log": false
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "iteration_log": false
+            },
+            {
+                "_target_": "CheckpointSaver",
+                "save_dir": "@ckpt_dir",
+                "save_dict": {
+                    "model": "@network"
+                },
+                "save_key_metric": true,
+                "key_metric_filename": "model.pt"
+            }
+        ],
+        "key_metric": {
+            "val_mean_dice": {
+                "_target_": "MeanDice",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "additional_metrics": {
+            "ar/dice": {
+                "_target_": "MeanDice",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred_ar', 'label_ar'])"
+            },
+            "ve/dice": {
+                "_target_": "MeanDice",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred_ve', 'label_ve'])"
+            },
+            "ur/dice": {
+                "_target_": "MeanDice",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred_ur', 'label_ur'])"
+            },
+            "ki/dice": {
+                "_target_": "MeanDice",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred_ki', 'label_ki'])"
+            },
+            "tu/dice": {
+                "_target_": "MeanDice",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred_tu', 'label_tu'])"
+            },
+            "tu/haunsdorff": {
+                "_target_": "HausdorffDistance",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred_tu', 'label_tu'])"
+            },
+            "tu/surface": {
+                "_target_": "SurfaceDistance",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred_tu', 'label_tu'])"
+            }
+        },
+        "evaluator": {
+            "_target_": "SupervisedEvaluator",
+            "device": "@device",
+            "val_data_loader": "@validate#dataloader",
+            "network": "@network",
+            "inferer": "@validate#inferer",
+            "postprocessing": "@validate#postprocessing",
+            "key_val_metric": "@validate#key_metric",
+            "additional_metrics": "@validate#additional_metrics",
+            "val_handlers": "@validate#handlers",
+            "amp": true
+        }
+    },
+    "training": [
+        "$monai.utils.set_determinism(seed=42)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@train#trainer.run()"
+    ]
+}

docs/README.md

@@ -0,0 +1,134 @@
+# Model Title
+Renal structures CECT segmentation
+
+### **Authors**
+Ivan Chernenkiy,   Michael Chernenkiy,   Dmitry Fiev,   Evgeny Sirota, Center for Neural Network Technologies / Institute of Urology and Human Reproductive Systems / Sechenov First Moscow State Medical University
+
+### **Tags**
+Segmentation, CT, CECT, Kidney, Renal, Supervised
+
+## **Model Description**
+The model is the SegResNet architecture[1] for volumetric (3D) renal structures segmentation. Input is artery, vein, excretory phases after mutual registration and concatenated to 3 channel 3D tensor.
+
+
+## **Data**
+DICOM data from 41 patients with kidney neoplasms were used [2]. The images and segmentation data are available under a CC BY-NC-SA 4.0 license. Data included all phases of contrast-enhanced multispiral computed tomography. We split the data: 32 observations for the training set and 9 – for the validation set. At the labeling stage, the arterial, venous, and excretory phases were taken, affine registration was performed to jointly match the location of the kidneys, and noise was removed using a median filter and a non-local means filter. Validation set ip published to Yandex.Disk. You can download via [link](https://disk.yandex.ru/d/pWEKt6D3qi3-aw) or use following command:
+```bash
+python -m monai.bundle run download_data --meta_file configs/metadata.json --config_file "['configs/train.json', 'configs/evaluate.json']"
+```
+
+**NB**: underlying data is in LPS orientation. IF! you want to test model on your own data, reorient it from RAS to LPS with `Orientation` transform. You can see example of preprocessing pipeline in `inference.json` file of this bundle.
+
+#### **Preprocessing**
+Images are (1) croped to kidney region, all (artery,vein,excret) phases are (2) [registered](https://simpleitk.readthedocs.io/en/master/registrationOverview.html#lbl-registration-overview) with affine transform, noise removed with (3) median and (4) non-local means filter. After that, images are (5) resampled to (0.8,0.8,0.8) density and intesities are (6) scaled from [-1000,1000] to [0,1] range.
+
+## **Performance**
+On the validation subset, the values of the Dice score of the SegResNet architecture were: 0.89 for the normal parenchyma of the kidney, 0.58 for the kidney neoplasms, 0.86 for arteries, 0.80 for veins, 0.80 for ureters.
+
+When compared with the nnU-Net model, which was trained on KiTS 21 dataset, the Dice score was greater for the kidney parenchyma in SegResNet – 0.89 compared to three model variants: lowres – 0.69, fullres – 0.70, cascade – 0.69. At the same time, for the neoplasms of the parenchyma of the kidney, the Dice score was comparable: for SegResNet – 0.58, for nnU-Net fullres – 0.59; lowres and cascade had lower Dice score of 0.37 and 0.45, respectively. To reproduce, visit - https://github.com/blacky-i/nephro-segmentation
+
+
+## **Additional Usage Steps**
+
+#### Execute training:
+
+```bash
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json
+```
+Expected result: finished, Training process started
+
+
+#### Execute training with finetuning
+```bash
+python -m monai.bundle run training --dont_finetune false --meta_file configs/metadata.json --config_file configs/train.json
+```
+Expected result: finished, Training process started, model variables are restored
+
+#### Execute validation:
+
+Download validation data (described in [Data](#data) section).
+
+With provided model weights mean dice score is expected to be ~0.78446.
+
+#####  Run validation script:
+```bash
+python -m monai.bundle run evaluate --meta_file configs/metadata.json --config_file "['configs/train.json', 'configs/evaluate.json']"
+```
+Expected result: finished, `Key metric: val_mean_dice best value: ...` is printed.
+
+## **System Configuration**
+The model was trained for 10000 epochs on 2 RTX2080Ti GPUs with [SmartCacheDataset](https://docs.monai.io/en/stable/data.html#smartcachedataset). This takes 1 days and 2 hours, with 4 images per GPU.
+Training progress is available on [tensorboard.dev](https://tensorboard.dev/experiment/VlEMjLdURH6SyFp216dFBg)
+
+To perform training in minimal settings, at least one 12GB-memory GPU is required.
+Actual Model Input: 96 x 96 x 96
+
+## **Limitations**
+For developmental purposes only and cannot be used directly for clinical procedures.
+
+## **Citation Info**
+```
+@article{chernenkiy2023segmentation,
+  title={Segmentation of renal structures based on contrast computed tomography scans using a convolutional neural network},
+  author={Chernenkiy, IМ and Chernenkiy, MM and Fiev, DN and Sirota, ES},
+  journal={Sechenov Medical Journal},
+  volume={14},
+  number={1},
+  pages={39--49},
+  year={2023}
+}
+```
+
+## **References**
+
+[1] Myronenko, A. (2019). 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science(), vol 11384. Springer, Cham. https://doi.org/10.1007/978-3-030-11726-9_28
+
+[2] Chernenkiy, I. М., et al. "Segmentation of renal structures based on contrast computed tomography scans using a convolutional neural network." Sechenov Medical Journal 14.1 (2023): 39-49.https://doi.org/10.47093/2218-7332.2023.14.1.39-49
+
+#### **Tests used for bundle checking**
+
+Checking with ci script file
+```bash
+python ci/verify_bundle.py -b renalStructures_CECT_segmentation -p models
+```
+Expected result: passed, model.pt file downloaded
+
+
+Checking downloading validation data file
+```bash
+cd models/renalStructures_CECT_segmentation
+python -m monai.bundle run download_data --meta_file configs/metadata.json --config_file "['configs/train.json', 'configs/evaluate.json']"
+```
+Expected result: finished, `data/` folder is created and filled with images.
+
+
+Checking evaluation script
+```bash
+python -m monai.bundle run evaluate --meta_file configs/metadata.json --config_file "['configs/train.json', 'configs/evaluate.json']"
+```
+Expected result: finished, `Key metric: val_mean_dice best value: ...` is printed.
+
+
+Checking train script
+```bash
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json
+```
+Expected result: finished, Training process started
+
+
+Checking train script with finetuning
+```bash
+python -m monai.bundle run training --dont_finetune false --meta_file configs/metadata.json --config_file configs/train.json
+```
+Expected result: finished, Training process started, model variables are restored
+
+Checking inference script
+```bash
+python -m monai.bundle run inference --meta_file configs/metadata.json --config_file configs/inference.json
+```
+Expected result: finished, in `eval` folder masks are created
+
+Check unit test with script:
+```bash
+python ci/unit_tests/runner.py --b renalStructures_CECT_segmentation
+```

docs/data_license.txt

@@ -0,0 +1,463 @@
+Third Party Licenses
+-----------------------------------------------------------------------
+
+/*********************************************************************/
+i. Validation dataset of renal structures
+    https://www.med.upenn.edu/sbia/brats2018/data.html
+/*********************************************************************/
+
+Data Usage Agreement / Citations
+The data is under a CC BY-NC-SA (Attribution-NonCommercial-ShareAlike)
+license.
+You are free to use and/or refer in your own research, provided that
+you always cite the following manuscript:
+
+[1] Chernenkiy, I. М., Chernenkiy, M. M., Fiev, D. N., & Sirota, E. S.
+"Segmentation of renal structures based on contrast computed tomography
+scans using a convolutional neural network." Sechenov Medical Journal
+14.1 (2023): 39-49.
+DOI: https://doi.org/10.47093/2218-7332.2023.14.1.39-49
+
+/*********************************************************************/
+i.License text
+/*********************************************************************/
+
+=======================================================================
+
+Attribution-NonCommercial-ShareAlike 4.0 International
+
+=======================================================================
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models/model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef934ae5b6fdf9a19b83ab9ec28b64d63dca43aea8ef3e177724c3076170a21f
+size 75925274

scripts/__init__.py

@@ -0,0 +1,2 @@
+from .download_data import download_cect_data
+from .my_transforms import ConcatImages

scripts/download_data.py

@@ -0,0 +1,69 @@
+import json
+import logging
+import os
+import sys
+import threading
+import time
+import zipfile
+from urllib.parse import urlencode
+from urllib.request import urlopen, urlretrieve
+
+
+# https://stackoverflow.com/a/39504463
+class Spinner:
+    busy = False
+    delay = 0.1
+
+    @staticmethod
+    def spinning_cursor():
+        while 1:
+            for cursor in "|/-\\":
+                yield cursor
+
+    def __init__(self, delay=None):
+        self.spinner_generator = self.spinning_cursor()
+        if delay and float(delay):
+            self.delay = delay
+
+    def spinner_task(self):
+        while self.busy:
+            sys.stdout.write(next(self.spinner_generator))
+            sys.stdout.flush()
+            time.sleep(self.delay)
+            sys.stdout.write("\b")
+            sys.stdout.flush()
+
+    def __enter__(self):
+        self.busy = True
+        threading.Thread(target=self.spinner_task).start()
+
+    def __exit__(self, exception, value, tb):
+        self.busy = False
+        time.sleep(self.delay)
+        if exception is not None:
+            return False
+
+
+def download_cect_data(bundle_root: str = "."):
+    base_url = "https://cloud-api.yandex.net/v1/disk/public/resources/download?"
+    public_key = "https://disk.yandex.ru/d/pWEKt6D3qi3-aw"
+
+    url = base_url + urlencode(dict(public_key=public_key))
+    response = urlopen(url)
+    filepath = "AVUCTK_cases.zip"
+    if not os.path.exists(os.path.join(bundle_root, filepath)):
+        with urlopen(url) as response:
+            code = response.getcode()
+            if code == 200:
+                download_url = json.loads(response.read())["href"]
+                print("Downloading file...")
+                with Spinner():
+                    urlretrieve(download_url, filepath)
+            else:
+                raise RuntimeError(
+                    f"Download of file from {url} to {filepath} failed due to network issue or denied permission."
+                )
+    else:
+        logging.info("zipfile exists, skipping download")
+    with zipfile.ZipFile(os.path.join(bundle_root, filepath), "r") as zip_ref:
+        zip_ref.extractall()

scripts/my_transforms.py

@@ -0,0 +1,49 @@
+import numpy as np
+import torch
+from monai.transforms import InvertibleTransform
+from monai.transforms.transform import MapTransform
+
+
+class ConcatImages(MapTransform, InvertibleTransform):
+    def __init__(self, keys_merge, keys_out, allow_missing_keys=True):
+        self.keys_merge = keys_merge
+        self.keys_out = keys_out
+        self.key_target_meta = keys_merge[0] + "_meta_dict"
+        self.allow_missing_keys = allow_missing_keys
+
+    def __call__(self, data):
+        if isinstance(data, list):
+            for data_row in data:
+                data_row[self.keys_out] = np.concatenate([data_row[key] for key in self.keys_merge])
+                data_row[self.keys_out + "_meta_dict"] = data_row[self.key_target_meta]
+        else:
+            data[self.keys_out] = np.concatenate([data[key] for key in self.keys_merge])
+            data[self.keys_out + "_meta_dict"] = data[self.key_target_meta]
+        return data
+
+    def inverse(self, data):
+        return data
+
+
+class MergeClassesd(MapTransform):
+    def __call__(self, data):
+        for key in self.keys:
+            if key in data:
+                num_classes = data[key].size(-4)
+                device = data[key].device
+                merged = None
+                for channel in data[key].squeeze() * torch.tensor(list(range(num_classes)), device=device).view(
+                    -1, 1, 1, 1
+                ):
+                    imgvol = channel
+                    if merged is not None:
+                        merged = merged + imgvol * ~((merged != 0) & (imgvol != 0))
+                    else:
+                        merged = imgvol
+                data[key] = merged.unsqueeze(0)
+            elif not self.allow_missing_keys:
+                raise KeyError(
+                    f"Key `{key}` of transform `{self.__class__.__name__}` was missing in the data"
+                    " and allow_missing_keys==False."
+                )
+        return data