|
--- |
|
language: |
|
- en |
|
license: apache-2.0 |
|
library_name: atommic |
|
datasets: |
|
- SKMTEA |
|
thumbnail: null |
|
tags: |
|
- image-segmentation |
|
- UNet3D |
|
- ATOMMIC |
|
- pytorch |
|
model-index: |
|
- name: SEG_UNet3D_SKMTEA |
|
results: [] |
|
|
|
--- |
|
|
|
|
|
## Model Overview |
|
|
|
AttentionUNet for MRI Segmentation on the SKMTEA dataset. |
|
|
|
|
|
## ATOMMIC: Training |
|
|
|
To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. |
|
``` |
|
pip install atommic['all'] |
|
``` |
|
|
|
## How to Use this Model |
|
|
|
The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. |
|
|
|
Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/SKMTEA/conf). |
|
|
|
### Automatically instantiate the model |
|
|
|
```base |
|
pretrained: true |
|
checkpoint: https://huggingface.co/wdika/SEG_UNet3D_SKMTEA/blob/main/SEG_UNet3D_SKMTEA.atommic |
|
mode: test |
|
``` |
|
|
|
### Usage |
|
|
|
You need to download the SKM-TEA dataset to effectively use this model. Check the [SKMTEA](https://github.com/wdika/atommic/blob/main/projects/SEG/SKMTEA/README.md) page for more information. |
|
|
|
|
|
## Model Architecture |
|
```base |
|
model: |
|
model_name: SEGMENTATION3DUNET |
|
segmentation_module: UNet |
|
segmentation_module_input_channels: 1 |
|
segmentation_module_output_channels: 4 |
|
segmentation_module_channels: 32 |
|
segmentation_module_pooling_layers: 5 |
|
segmentation_module_dropout: 0.0 |
|
segmentation_module_normalize: false |
|
segmentation_loss: |
|
dice: 1.0 |
|
dice_loss_include_background: true # always set to true if the background is removed |
|
dice_loss_to_onehot_y: false |
|
dice_loss_sigmoid: false |
|
dice_loss_softmax: false |
|
dice_loss_other_act: none |
|
dice_loss_squared_pred: false |
|
dice_loss_jaccard: false |
|
dice_loss_flatten: false |
|
dice_loss_reduction: mean_batch |
|
dice_loss_smooth_nr: 1e-5 |
|
dice_loss_smooth_dr: 1e-5 |
|
dice_loss_batch: true |
|
dice_metric_include_background: true # always set to true if the background is removed |
|
dice_metric_to_onehot_y: false |
|
dice_metric_sigmoid: false |
|
dice_metric_softmax: false |
|
dice_metric_other_act: none |
|
dice_metric_squared_pred: false |
|
dice_metric_jaccard: false |
|
dice_metric_flatten: false |
|
dice_metric_reduction: mean_batch |
|
dice_metric_smooth_nr: 1e-5 |
|
dice_metric_smooth_dr: 1e-5 |
|
dice_metric_batch: true |
|
segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5] |
|
segmentation_activation: sigmoid |
|
magnitude_input: true |
|
log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated |
|
normalization_type: minmax |
|
normalize_segmentation_output: true |
|
complex_data: false |
|
``` |
|
|
|
## Training |
|
```base |
|
optim: |
|
name: adam |
|
lr: 1e-4 |
|
betas: |
|
- 0.9 |
|
- 0.98 |
|
weight_decay: 0.0 |
|
sched: |
|
name: InverseSquareRootAnnealing |
|
min_lr: 0.0 |
|
last_epoch: -1 |
|
warmup_ratio: 0.1 |
|
|
|
trainer: |
|
strategy: ddp_find_unused_parameters_false |
|
accelerator: gpu |
|
devices: 1 |
|
num_nodes: 1 |
|
max_epochs: 20 |
|
precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16' |
|
enable_checkpointing: false |
|
logger: false |
|
log_every_n_steps: 50 |
|
check_val_every_n_epoch: -1 |
|
max_steps: -1 |
|
``` |
|
|
|
## Performance |
|
|
|
Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice. |
|
|
|
Results |
|
------- |
|
|
|
Evaluation |
|
---------- |
|
DICE = 0.9175 +/- 0.06793 F1 = 0.7889 +/- 0.404 HD95 = 5.893 +/- 2.995 IOU = 0.5301 +/- 0.347 |
|
|
|
|
|
## References |
|
|
|
[1] [ATOMMIC](https://github.com/wdika/atommic) |
|
|
|
[2] Desai AD, Schmidt AM, Rubin EB, et al. SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation. 2022 |
