Medical Image Labeler
Interactively explore, label, and publish animations of 2-D or 3-D medical image data
The Medical Image Labeler app enables you to label ground truth data in medical images. Using the app, you can:
Import multiple 2-D images or 3-D image volumes.
Explore images as slice planes or volumes with anatomical orientation markers and scale bars. Navigate volume slices using crosshairs.
Publish 2-D and 3-D PNG or PDF image snapshots and GIF animations.
Create multiple pixel label definitions to label regions of interest. Label pixels using automatic algorithms such as flood fill, semi-automatic techniques such as interpolation, and manual techniques such as painting by superpixels.
Write, import, and use your own custom automation algorithm to automatically label ground truth data.
Export the labeled ground truth data as a
groundTruthMedicalobject. You can use this object to share labels with colleagues or for training semantic segmentation deep learning networks.
The Medical Image Labeler app supports 2-D images and image sequences stored in the DICOM and NIfTI file formats. An image sequence is a series of images related by time, such as ultrasound data. The app supports 3-D image volume data stored in the DICOM (single or multifile volume), NIfTI, and NRRD file formats.
To learn more about this app, see Get Started with Medical Image Labeler.
The app is not supported in MATLAB® Online™. For details, see Specifications and Limitations.
Open the Medical Image Labeler App
MATLAB Toolstrip: On the Apps tab, under Image Processing and Computer Vision, click the Medical Image Labeler app icon.
MATLAB command prompt: Enter
- Get Started with Medical Image Labeler
- Visualize 3-D Medical Image Data Using Medical Image Labeler
- Label 2-D Ultrasound Series Using Medical Image Labeler
- Label 3-D Medical Image Using Medical Image Labeler
- Automate Labeling in Medical Image Labeler
- Collaborate on Multi-Labeler Medical Image Labeling Projects
medicalImageLabeler opens the Medical Image Labeler app,
which enables you to start a new session to label 2-D or 3-D medical image data.
medicalImageLabeler( opens the app
and loads the image and label data stored in the file
gTruthFile into the
gTruthFile is the full path to a MAT file containing a
groundTruthMedical object, specified as a string scalar or character
medicalImageLabeler( opens the app and
loads the image and label data stored in the
gTruth from the workspace into the app.
medicalImageLabeler( opens the
app and loads a saved labeling session into the app, where
sessionFolder is the full path to a session folder created using the
Medical Image Labeler app.
medicalImageLabeler( opens the app
and creates a new session of the specified type, where
"Image". For more information about
session types, see Get Started with Medical Image Labeler.
Version HistoryIntroduced in R2022b
R2023a: Generate animations and display slice crosshairs
The app includes these new capabilities:
Animation Generator — Configure, preview, and export animations from the app. In a volume session, generate animations that loop through slices in the coronal, sagittal, or transverse plane, or show a rotating 3-D volume. In an image session, generate animations that loop through the frames of an image series, such as an ultrasound video.
Optionally include labeled regions in animations, and customize parameters such as step size, loop count, and total animation length in seconds. To open the Animation pane, in the app toolstrip, click Generate Animations.
Crosshair Navigation — In a volume session, view and navigate 2-D slice positions using crosshairs. To turn on the crosshair indicators, in the app toolstrip, select Crosshair Navigation. The indicators show the relative positions of the slices in the other 2-D views. To navigate slices, pause on a crosshair until the cursor changes to the fleur shape, , and then click and drag to a new position. The other slice views update automatically. To hide the crosshairs, in the app toolstrip, clear Crosshair Navigation.
groundTruthMedical | Image Labeler (Computer Vision Toolbox)