Create Datastores for Medical Image Semantic Segmentation
Semantic segmentation deep learning networks segment a medical image by assigning a class
label, such as
lung, to every pixel in the
image. To train a semantic segmentation network, you must have a collection of images, or
data sources, and a collection of label images that contain labels
for the pixels in the data source images. Manage training data for semantic segmentation by
Medical Image Ground Truth Data
You can use the Medical Image
Labeler app to label 2-D or 3-D medical images to generate training data for
semantic segmentation networks. The app stores labeling results in a
groundTruthMedical object, which specifies the filenames of data source and
pixel label images in its
properties, respectively. The table shows how a
formats the data source and label image information for 2-D versus 3-D data.
|Type of Data||Data Source Format||Label Data Format|
|2-D medical images or multiframe 2-D image series|
|3-D medical image volumes|
Datastores for Semantic Segmentation
You can perform medical image semantic segmentation using 2-D or 3-D deep learning networks. A 2-D network accepts 2-D input images and predicts segmentation labels using 2-D convolution kernels. The input images can be one of these sources:
Images from 2-D modalities, such as X-ray.
Individual frames extracted from a multiframe 2-D image series, such as an ultrasound video.
Individual slices extracted from a 3-D image volume, such as a CT or MRI scan.
A 3-D network accepts 3-D input images and predicts segmentation labels using 3-D convolution kernels. The input images are 3-D medical volumes, such as entire CT or MRI volumes.
The benefits of 2-D networks include faster prediction speeds and lower memory requirements. Additionally, you can generate many 2-D training images from one image volume or series. Therefore, fewer scans are required to train a 2-D network that segments a volume slice-by-slice versus training a fully 3-D network. The major benefit of 3-D networks is that they use information from adjacent slices or frames to predict segmentation labels, which can produce more accurate results.
For an example that shows how to create datastores that contain 2-D ultrasound frames, see Convert Ultrasound Image Series into Training Data for 2-D Semantic Segmentation Network.
For an example that shows how to create, preprocess, and augment 3-D datastores for segmentation, see Create Training Data for 3-D Medical Image Semantic Segmentation.