Definiens in Medical Imaging

6
Definiens in Medical Imaging
Example: Case-based retrieval in mammography
Definiens developed a prototype which extracts breast, nipples, calcifi cations and
masses from x-ray mammograms. The statistical results can be used for a similarity
search in a case database.
The image data is analyzed via the following steps:
1. Accurate segmentation of object of interest
2. Detection and classification of masses and calcifications and creation of typical
fingerprint including lesion morphology
3. Recording the results in a table
4. Comparison of the actual case with similar cases in the table
5. Presentation of similar cases
The Results
The prototype was applied to 28 cancer cases. In 6 cases, clustered calcifications were
the only indications of cancer. In 8 cases malignant masses indicated cancer. The
remaining 14 cases had calcifications within masses.
The Definiens‘ approach detected at least one cancer region in 92.9 % of the cancer
cases.Where there were malignant calcification clusters, the algorithm detected at least
one cluster in all cases. The detection rate for cancer cases that contained malignant
masses was 90.9 %.
In a multi-modal approach, images from different image modalities are analyzed
in parallel. The corresponding prototype can link tumor objects to corresponding
objects in images of the same or a different modality. Representative statistical results,
for example on tumor morphology can be exported to support an expert’s diagnosis.
Conclusions
The results on different data modalities performing Computer Aided Detection of
breast lesions look very promising. The technology will be the basis for the creation
of new generation of CAD Systems, not only for the breast, but also for other organs by
using different patient-relevant data in parallel.
Figure 5 Screenshot of linked image information, with
a1) US-, a2) x-ray- and a3) MR-mammography, analyzed
by using a CNL prototype, which consists of a process
hierarchy b1) and a class hierarchy b2). Tumors are linked
to each other (outlined in red)
Acknowledgment
This work was supported by the Bayerische For schungsstiftung
within the scope of the project Mammo-iCAD.