  Ultrasound (US) is the most commonly used liver imaging modality worldwide.
It plays an important role in follow-up of cancer patients with liver
metastases. We present an interactive segmentation approach for liver tumors in
US acquisitions. Due to the low image quality and the low contrast between the
tumors and the surrounding tissue in US images, the segmentation is very
challenging. Thus, the clinical practice still relies on manual measurement and
outlining of the tumors in the US images. We target this problem by applying an
interactive segmentation algorithm to the US data, allowing the user to get
real-time feedback of the segmentation results. The algorithm has been
developed and tested hand-in-hand by physicians and computer scientists to make
sure a future practical usage in a clinical setting is feasible. To cover
typical acquisitions from the clinical routine, the approach has been evaluated
with dozens of datasets where the tumors are hyperechoic (brighter), hypoechoic
(darker) or isoechoic (similar) in comparison to the surrounding liver tissue.
Due to the interactive real-time behavior of the approach, it was possible even
in difficult cases to find satisfying segmentations of the tumors within
seconds and without parameter settings, and the average tumor deviation was
only 1.4mm compared with manual measurements. However, the long term goal is to
ease the volumetric acquisition of liver tumors in order to evaluate for
treatment response. Additional aim is the registration of intraoperative US
images via the interactive segmentations to the patient's pre-interventional CT
acquisitions.
