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nnUNet Lesion Segmentation

This repository contains the nnUNet model weights for lesion segmentation. The model was trained on large dataset with 3000+ cases, acquired on various scanners and field strengths. Lesion labels were acquired using semi-automatic lesion segmentation procedure.

Dataset

Dataset used for training consisted of FLAIR and T1 MRI images. Lesion masks were acquired semi-automatic.

Preprocessing

Preprocessing consisted of:

  1. T1, FLAIR, SEGMENTATION registration to MNI ICBM 152 atlas
  2. BACKGROUND removal
  3. CROPPING to shape divisible by two: (192, 224, 192)

nnUNet Configs

Dataset Configuration

  • Channel Names:
    • 0: FLAIR
    • 1: T1
  • Labels:
    • 0: Background
    • 1: Lesion

Training Configuration

  • Plans Name: nnUNetPlans
  • Original Median Spacing (after transpose): [1.0, 1.0, 1.0]
  • Original Median Shape (after transpose): [157, 183, 147]

3D Full Resolution Configuration

  • Data Identifier: nnUNetPlans_3d_fullres
  • Preprocessor Name: DefaultPreprocessor
  • Number of Epochs: 1000
  • Batch Size: 6
  • Patch Size: [128, 128, 128]
  • Median Image Size in Voxels: [157.0, 183.0, 147.0]
  • Spacing: [1.0, 1.0, 1.0]
  • Normalization Schemes: ZScoreNormalization (both channels)
  • Use Mask for Normalization: True (both channels)
  • UNet Class Name: PlainConvUNet
  • UNet Base Number of Features: 32
  • Number of Convolutions per Stage (Encoder): [2, 2, 2, 2, 2, 2]
  • Number of Convolutions per Stage (Decoder): [2, 2, 2, 2, 2]
  • Number of Pooling Operations per Axis: [5, 5, 5]
  • Pooling Operation Kernel Sizes: [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]]
  • Convolution Kernel Sizes: [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]
  • UNet Max Number of Features: 320
  • Resampling Function Data: resample_data_or_seg_to_shape
  • Batch Dice: False

Usage

Before using this model you need nnUNet V2. For this, check original repository documentation on instalation https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/installation_instructions.md

Predicting

For predicting dataset your call command should look like:

INPUT_FOLDER=<path-to-input-folder-in-nnunet-compatible-format>
OUTPUT_FOLDER=<path-to-output-folder>

nnUNetv2_predict -d 100 -i $INPUT_FOLDER -o $OUTPUT_FOLDER -f 0 -c 3d_fullres

Fine-Tuning

For fine-tuning custom model your command should look like:

nnUNetv2_train <task-id> 3d_fullres <fold-id> -pretrained_weights "/<path-to-source>/Dataset100_FLAIR-T1/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_best.pth"