Feature highlight

Check that studies are uploaded to correct subject

Clinical trial pain-point

Attaching labels to de-identified studies is a tedious and manual process in most clinical trials, and with all other manual processes it is prone to human error. Mislabeling of studies can have detrimental effects on overall study outcomes.

SliceVault solution

SliceVault uses artificial intelligences (AI) to ensure CT studies are labelled correctly. 



Study A

Study B


Same subject

Not same subject

Automated visual inspection

When correct labelling cannot be determined based on DICOM tags, since studies have been de-identified in most clinical trials, we resort to visual inspections. Sometimes this is an easy task, for example when studies differ in size, gender, or if only one has a hip prosthesis. In other situations, incorrect labelling can be easily overlooked, especially since most studies are labelled correctly and the image reader is focused primarily on endpoint assessment.

SliceVault comes with an AI-based visual inspection tool where incorrect labelled studies are flagged automatically. 

Step 1: Organs and skeletal structures are segmented automatically in CT studies by Organ Finder - SliceVault's embedded convolutional neural network.

Step 2: A surface mesh is extracted for each structure using the Surface Nets technique and meshes are aligned using the Iterative Closest Point method.

Determining anatomical match

With the aligned meshes, the anatomical match index describing the similarity between studies is calculated to determine whether different CT studies belong to the same subject or not.

Studies with an anatomical match of at least 95% in at least three meshes is classified as belonging to the same subject.

Example alignment of meshes of left scapula


Same subject

Not same subject

Not same subject