language:
- en
license: apache-2.0
library_name: atommic
datasets:
- ISLES2022SubAcuteStroke
thumbnail: null
tags:
- image-segmentation
- UNet3D
- ATOMMIC
- pytorch
model-index:
- name: SEG_UNet3D_ISLES2022SubAcuteStroke
results: []
Model Overview
AttentionUNet for MRI Segmentation on the ISLES2022SubAcuteStroke dataset.
ATOMMIC: Training
To train, fine-tune, or test the model you will need to install 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.
Automatically instantiate the model
pretrained: true
checkpoint: https://huggingface.co/wdika/SEG_UNet3D_ISLES2022SubAcuteStroke/blob/main/SEG_UNet3D_ISLES2022SubAcuteStroke.atommic
mode: test
Usage
You need to download the ISLES 2022 Sub Acute Stroke dataset to effectively use this model. Check the ISLES2022SubAcuteStroke page for more information.
Model Architecture
model:
model_name: SEGMENTATION3DUNET
segmentation_module: UNet
segmentation_module_input_channels: 3
segmentation_module_output_channels: 1
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 ]
segmentation_activation: sigmoid
magnitude_input: true
log_multiple_modalities: true # 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
optim:
name: adamw
lr: 1e-4
betas:
- 0.9
- 0.999
weight_decay: 0.0
sched:
name: CosineAnnealing
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: 50
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 script for the segmentation task, with --evaluation_type per_slice.
Results
Evaluation
ALD = 0.8206 +/- 2.167 AVD = 0.691 +/- 5.458 DICE = 0.6871 +/- 0.5468 L-F1 = 0.7982 +/- 0.5733
Limitations
This model was trained on the ISLES2022SubAcuteStroke dataset with stacked ADC, DWI, FLAIR images and might differ in performance compared to the leaderboard results.
References
[1] ATOMMIC
[2] Petzsche MRH, Rosa E de la, Hanning U, et al. ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Scientific Data 1 2022;9