Usage

Once you have installed it, you can start to segment the images directly from your bash. The input --dir is the path of the dir containing the DICOM series. Please ensure that the folder contains only one series. If the directory is a nested dir, the script will find automatically the sub-dir containing the DICOM series.

Quick start

python -m MRIsegm --dir='/path/to/input/series/'

where: --dir is the path of the directory containing the DICOM series (required).

Options

--model

Name of the model’s weights saved in the weights dir.

python -m MRIsegm --dir='/path/to/input/series/' --model='model_name'

notes:

• model_name set as default: efficientnetb0_BTC=4_full_150E_OPT=adam_LOSS=dice_loss_plus_1binary_focal_loss

• Remember to specify the name without _weights.h5

• you can also use your own model’s weight saving the weights in the weights dir as model_name_weights.h5.

• warning: You need to save also the architecture as model_name.json file in the same dir.

--mask

When enabled plot the predicted binary [0,1] mask of each slice.

python -m MRIsegm --dir='/path/to/input/series/' --mask

--density

When enabled plot the predicted probability map between 0 and 1 of each slice over the original image.

python -m MRIsegm --dir='/path/to/input/series/' --density

--3D

When enabled plot the a 3D mesh of the segmented areas.

python -m MRIsegm --dir='/path/to/input/series/' --3D