This model was created for participation in the Brain Tumor Segmentation Challenge (BraTS) 2019. It has the UNet architecture trained with residual blocks.
Metric | Value |
---|---|
Type | Segmentation |
GFLOPs | 300.801 |
MParams | 4.51 |
Source framework | PyTorch* |
See BRATS 2019 Leaderboard. The metrics for challenge validation (Dice_WT, Dice_TC, Dice_ET) differ from the metrics reported below (which are compartible with input labels):
The following accuracy metrics are measured on a brain tumor
training subset of the Medical Decathlon dataset.
Mean:
Median:
NOTE: The accuracy achieved with ONNX* model adapted for OpenVINO™ can slightly differ from the accuracy achieved with the original PyTorch model since the upsampling operation was changed from the
trilinear
tonearest
mode.
The model takes as an input four MRI modalities T1
, T1ce
, T2
, Flair
. Find additional information on the BraTS 2019 page and wiki. In the preprocessing pipeline, each modality should be z-score normalized separately. The input tensor is a concatenation of the four input modalities.
MR Image, name - 0
, shape - 1,4,128,128,128
, format is B,C,D,H,W
, where:
B
- batch sizeC
- channelD
- depthH
- heightW
- widthThe channels are ordered as T1
, T1ce
, T2
, Flair
.
MR Image, name - 0
, shape - 1,4,128,128,128
, format is B,C,D,H,W
, where:
B
- batch sizeC
- channelD
- depthH
- heightW
- widthThe channels are ordered as T1
, T1ce
, T2
, Flair
.
Probabilities of the given voxel to be in the corresponding class, name - 304
, shape - 1,3,128,128,128
, output data format is B,C,D,H,W
, where:
B
- batch sizeC
- channelD
- depthH
- heightW
- widthThe channels are ordered as whole tumor
, tumor core
, and enhancing tumor
.
Probabilities of the given voxel to be in the corresponding class, name - 304
, shape - 1,3,128,128,128
, output data format is B,C,D,H,W
, where:
B
- batch sizeC
- channelD
- depthH
- heightW
- widthThe channels are ordered as whole tumor
, tumor core
, and enhancing tumor
.
You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.
An example of using the Model Downloader:
An example of using the Model Converter:
The original model is distributed under the MIT License.