Model Overview

A pre-trained Swin UNETR [1,2] for volumetric (3D) multi-organ segmentation using CT images from Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset [3].

$\"model$

Data

The training data is from the BTCV dataset (Register through Synapse and download the Abdomen/RawData.zip).

Target: Multi-organs
Task: Segmentation
Modality: CT
Size: 30 3D volumes (24 Training + 6 Testing)

Preprocessing

The dataset format needs to be redefined using the following commands:

unzip RawData.zip\nmv RawData/Training/img/ RawData/imagesTr\nmv RawData/Training/label/ RawData/labelsTr\nmv RawData/Testing/img/ RawData/imagesTs\n

Training configuration

The training as performed with the following:

GPU: At least 32GB of GPU memory
Actual Model Input: 96 x 96 x 96
AMP: True
Optimizer: Adam
Learning Rate: 2e-4

Memory Consumption

Dataset Manager: CacheDataset
Data Size: 30 samples
Cache Rate: 1.0
Single GPU - System RAM Usage: 5.8G

Memory Consumption Warning

If you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate cache_rate in the configurations within range [0, 1] to minimize the System RAM requirements.

Input

1 channel

CT image

Output

14 channels:

0: Background
1: Spleen
2: Right Kidney
3: Left Kideny
4: Gallbladder
5: Esophagus
6: Liver
7: Stomach
8: Aorta
9: IVC
10: Portal and Splenic Veins
11: Pancreas
12: Right adrenal gland
13: Left adrenal gland

Performance

Dice score was used for evaluating the performance of the model. This model achieves a mean dice score of 0.82

Training Loss

$\"The$

Validation Dice

$\"A$

TensorRT speedup

The swin_unetr bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that 32-bit precision models are benchmarked with tf32 weight format.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

method	torch_tf32(ms)	torch_amp(ms)	trt_tf32(ms)	trt_fp16(ms)	speedup amp	speedup tf32	speedup fp16	amp vs fp16
model computation	123.64	123.77	93.22	42.87	1.00	1.33	2.88	2.89
end2end	5102	4895	2863	2835	1.04	1.78	1.80	1.73

Where:

model computation means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
end2end means run the bundle end-to-end with the TensorRT based model.
torch_tf32 and torch_amp are for the PyTorch models with or without amp mode.
trt_tf32 and trt_fp16 are for the TensorRT based models converted in corresponding precision.
speedup amp, speedup tf32 and speedup fp16 are the speedup ratios of corresponding models versus the PyTorch float32 model
amp vs fp16 is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.

This result is benchmarked under:

TensorRT: 10.3.0+cuda12.6
Torch-TensorRT Version: 2.4.0
CPU Architecture: x86-64
OS: ubuntu 20.04
Python version:3.10.12
CUDA version: 12.6
GPU models and configuration: A100 80G

MONAI Bundle Commands

In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.

For more details usage instructions, visit the MONAI Bundle Configuration Page.

Execute training:

python -m monai.bundle run --config_file configs/train.json\n

Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using --dataset_dir:

\n\">

python -m monai.bundle run --config_file configs/train.json --dataset_dir <actual dataset path>\n

Override the `train` config to execute multi-GPU training:

torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\"\n

Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove --standalone, modify --nnodes, or do some other necessary changes according to the machine used. For more details, please refer to pytorch's official tutorial.

Override the `train` config to execute evaluation with the trained model:

python -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json']\"\n

Execute inference:

python -m monai.bundle run --config_file configs/inference.json\n

Execute inference with the TensorRT model:

python -m monai.bundle run --config_file \"['configs/inference.json', 'configs/inference_trt.json']\"\n

References

[1] Hatamizadeh, Ali, et al. \"Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images.\" arXiv preprint arXiv:2201.01266 (2022). https://arxiv.org/abs/2201.01266.

[2] Tang, Yucheng, et al. \"Self-supervised pre-training of swin transformers for 3d medical image analysis.\" arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791.

[3] Landman B, et al. \"MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge.\" In Proc. of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge 2015 Oct (Vol. 5, p. 12).

License

Licensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0\n

Unless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.

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This model achieves a mean dice score of 0.82\n\n##### Training Loss\n\n![The figure shows the training loss curve for 10K iterations.](https://developer.download.nvidia.com/assets/Clara/Images/monai_swin_unetr_btcv_segmentation_train_loss_v2.png)\n\n##### Validation Dice\n\n![A graph showing the validation mean Dice for 5000 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_swin_unetr_btcv_segmentation_val_dice_v2.png)\n\n##### TensorRT speedup\n\nThe `swin_unetr` bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that 32-bit precision models are benchmarked with tf32 weight format.\n\n| method | torch_tf32(ms) | torch_amp(ms) | trt_tf32(ms) | trt_fp16(ms) | speedup amp | speedup tf32 | speedup fp16 | amp vs fp16|\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| model computation | 123.64 | 123.77 | 93.22 | 42.87 | 1.00 | 1.33 | 2.88 | 2.89 |\n| end2end | 5102 | 4895 | 2863 | 2835 | 1.04 | 1.78 | 1.80 | 1.73 |\n\nWhere:\n- `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing\n- `end2end` means run the bundle end-to-end with the TensorRT based model.\n- `torch_tf32` and `torch_amp` are for the PyTorch models with or without `amp` mode.\n- `trt_tf32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.\n- `speedup amp`, `speedup tf32` and `speedup fp16` are the speedup ratios of corresponding models versus the PyTorch float32 model\n- `amp vs fp16` is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.\n\nThis result is benchmarked under:\n- TensorRT: 10.3.0+cuda12.6\n- Torch-TensorRT Version: 2.4.0\n- CPU Architecture: x86-64\n- OS: ubuntu 20.04\n- Python version:3.10.12\n- CUDA version: 12.6\n- GPU models and configuration: A100 80G\n\n### MONAI Bundle Commands\n\nIn addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.\n\nFor more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).\n\n##### Execute training:\n\n```\npython -m monai.bundle run --config_file configs/train.json\n```\n\nPlease note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:\n\n```\npython -m monai.bundle run --config_file configs/train.json --dataset_dir \n```\n\n##### Override the `train` config to execute multi-GPU training:\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\"\n```\n\nPlease note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).\n\n##### Override the `train` config to execute evaluation with the trained model:\n\n```\npython -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json']\"\n```\n\n##### Execute inference:\n\n```\npython -m monai.bundle run --config_file configs/inference.json\n```\n\n##### Execute inference with the TensorRT model:\n\n```\npython -m monai.bundle run --config_file \"['configs/inference.json', 'configs/inference_trt.json']\"\n```\n\n## References\n\n[1] Hatamizadeh, Ali, et al. \"Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images.\" arXiv preprint arXiv:2201.01266 (2022). https://arxiv.org/abs/2201.01266.\n\n[2] Tang, Yucheng, et al. \"Self-supervised pre-training of swin transformers for 3d medical image analysis.\" arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791.\n\n[3] Landman B, et al. \"MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge.\" In Proc. of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge 2015 Oct (Vol. 5, p. 12).\n\n## License\n\nCopyright (c) MONAI Consortium\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697],[644,645,646,647,648],{"_483":558,"_297":558,"_248":249,"_456":668},{"_483":532,"_297":532,"_248":249,"_456":663},{"_483":658,"_297":658,"_248":249,"_456":659},{"_483":653,"_297":653,"_248":249,"_456":654},{"_483":649,"_297":649,"_248":249,"_456":650},"nvidia.partners",[651],{"_81":652,"_489":649,"_490":31,"_248":526},"edge","app.hushly.com",[655],{"_81":656,"_489":653,"_490":31,"_248":657},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[660],{"_81":661,"_489":658,"_490":31,"_248":662},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784],[736,737,738,739],{"_483":751,"_297":751,"_248":249,"_456":752},{"_483":746,"_297":746,"_248":249,"_456":747},{"_483":545,"_297":545,"_248":249,"_456":743},{"_483":658,"_297":658,"_248":249,"_456":740},[741],{"_81":742,"_489":658,"_490":167,"_248":662},"_gh_sess",[744],{"_81":745,"_489":545,"_490":167,"_248":552},"YSC","bizible.com",[748],{"_81":749,"_489":746,"_490":31,"_248":750},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[753],{"_81":754,"_489":751,"_490":31,"_248":755},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_81":826,"_489":604,"_490":31,"_248":827},{"_81":811,"_489":825,"_490":167,"_248":812},{"_81":794,"_489":607,"_490":31,"_248":719},{"_81":794,"_489":707,"_490":31,"_248":719},{"_81":823,"_489":604,"_490":31,"_248":824},{"_81":811,"_489":610,"_490":167,"_248":812},{"_81":821,"_489":604,"_490":31,"_248":822},{"_81":811,"_489":607,"_490":167,"_248":812},{"_81":819,"_489":706,"_490":31,"_248":820},{"_81":811,"_489":728,"_490":167,"_248":812},{"_81":791,"_489":607,"_490":31,"_248":793},{"_81":817,"_489":604,"_490":167,"_248":818},{"_81":815,"_489":604,"_490":31,"_248":816},{"_81":813,"_489":604,"_490":167,"_248":814},{"_81":811,"_489":702,"_490":167,"_248":812},{"_81":809,"_489":604,"_490":31,"_248":810},{"_81":807,"_489":604,"_490":31,"_248":808},{"_81":805,"_489":604,"_490":167,"_248":806},{"_81":803,"_489":711,"_490":31,"_248":804},{"_81":791,"_489":702,"_490":31,"_248":793},{"_81":801,"_489":604,"_490":31,"_248":802},{"_81":799,"_489":604,"_490":167,"_248":800},{"_81":797,"_489":604,"_490":31,"_248":798},{"_81":795,"_489":604,"_490":31,"_248":796},{"_81":794,"_489":604,"_490":31,"_248":719},{"_81":791,"_489":792,"_490":31,"_248":793},{"_81":789,"_489":604,"_490":31,"_248":790},{"_81":787,"_489":604,"_490":167,"_248":788},{"_81":785,"_489":604,"_490":31,"_248":786},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. 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Name	Size	Updated	Actions



LICENSE	11.09 KB	February 28, 2025 UTC	curl wget Download