atommic image-segmentation UNet3D ATOMMIC pytorch en dataset:SKMTEA license:apache-2.0 region:us
---
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