Radiology AI: Comparison of best Dicom data labeling tools for ML

Dicom file format is the most commonly used file format for storing radiology image data. All radiology equipment like CT and MRI machines, manufactured by many companies, generate dicom images that are sent to a pacs server. Automatic analysis by various AI algorithms happens on the PAC server. The images and the results of automatic analysis are then pushed to individual radiologist terminals. Radiologists use automatic analysis done by AI algorithm to speed up their workflow.

On the development side, ML engineers use images from PAC server to create data-labeling pipelines. Human annotators provide segmentation and classification labeled data on top of dicom images. The selection of data labeling tools plays a critical role. A good dicom labeling tool allows effective collaboration between the radiologist and the data science team. It provides necessary features for radiologists to be able to read dicom data. Few examples are window level presets, multi-planar views. Without support of these features radiologists cannot view all details in dicom imagery.

Here is a comparison of best dicom image data labeling tools for data science teams. The three tools that support native dicom images are TrainingData.io, Labelbox and V7 darwin.

Feature comparison table

TrainingData.io

TrainingData.io provides dicom viewer similar to many industrial dicom viewers used by radiologists in their daily practice. Here are some unique features supported by this labeling tool:

1. Window level presets include support for Liver, Lungs, Bone, Subdural brain, Abdominal soft tissue. User can drag a slider for the window center and a slider for window width to set custom values for each.
2. Mutli-planar view allows radiologist to view axial plane, sagital plane, coronal plane in one view. It can be configured as 2x1, 1x2, or 2x2 windows.
3. Superpixel segmentation with brush and eraser is the fastest and most accurate tool for creating segmentation labels.
4. Classification is supported for DICOM data files.
5. Tags for each individual image
6. Cloud storage supported includes Amazon-S3 and OTC cloud from Deutsche Telekom

V7 Labs Darwin

In the samples that were made available, as shown below, the auto annotation feature looks impressive.

There is no support for Window level presets, multi-planar views, classification for dicom.

Labelbox

LabelBox does not provide support for dicom format directly. This feature is only available to select customers. Users need to convert dicom files to png or jpeg format.

LabelBox is one of the most popular image and video labeling tools in market.