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@@ -9,10 +9,43 @@ for enhanced computer-aided diagnosis of prostate cancer."
 Scientific Reports 13.1 (2023): 19805.
 ```
 
+## Usage
+The nnU-Net trainer `nnUNetTrainer_Misalign.py` generates possible misalignments/registration errors
+between the input image modalities/channels during training to teach the network to become robust
+to them thereby increasing their performance. The extension is simple, we just appended the
+`tr_transforms` with the `MisalignTransform` transformation with the following parameters:
+   - `im_channels_2_misalign`: on which image channels should the transformation be applied
+   - `label_channels_2_misalign`: on which segmentation channels should the transformation be applied
+   - `do_squeeze`: whether misalignment resulted from squeezing is necessary
+   - `sq_x`, `sq_y`, `sq_z`: probability of the transformation per organs
+   - `p_sq_per_sample`: probability of the transformation per sample
+   - `p_sq_per_dir`: probability of the transformation per direction
+   - `do_rotation`: whether misalignment resulted from rotation is necessary
+   - `angle_x`, `angle_y`, `angle_z`: rotation angels per axes, randomly sampled from interval.
+   - `p_rot_per_sample`: probability of the transformation per sample
+   - `p_rot_per_axis`: probability of the transformation per axes
+   - `do_transl`: whether misalignment resulted from rotation is necessary
+   - `tr_x`, `tr_y`, `tr_z`: shift/translation per directions, randomly sampled from interval.
+   - `p_transl_per_sample`: probability of the transformation per sample
+   - `p_transl_per_dir`: probability of the transformation per direction
+
+**Important suggestions for its successful utilization:**
+
+The ability of the method to cope with misalignments is limited depending on the initial overlap between
+the image modalities, and the type and amplitude of the augmentations. Therefore, its parameters have to be
+adapted to the exact application, consider the following aspects:
+* An initial registration might still be needed.
+* There might be misalignments from other sources in your dataset, you can extend the augmentation scheme
+with them.
+* Choose the probability and the amplitude of the augmentation wisely.
+
+Misalignment augmentation is originally proposed to enhance diagnostic performance, but you can use it
+for other multi-modal/multi-channel applications.
+
 ## Installation
 The repository is forked from nnU-Net v2, but contains no pretrained network so it can be used as an integrative
-framework for model developement
-1) Create a new conda new conda environment with the recent version of Python (nnU-Net v2 supports 3.9 or newer version),
+framework for model development
+1) Create a new conda environment with the recent version of Python (nnU-Net v2 supports 3.9 or newer version),
 as an example: `conda create --name nnUNet_AnatInf python=3.9`
 2) Install pytorch with the most recent CUDA version by following the instructions on the
 [PyTorch Website](https://pytorch.org/get-started/locally/).