US-Cut: interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions

• DOI: 10.1117/12.2216509
• Publication Date: 2016-04-01T22:37:18Z
• Authors: Jan Egger; Philip Voglreiter; Mark Dokter; Michael Hofmann; Xiaojun Chen; Wolfram G. Zoller; Dieter Schmalstieg; Alexander Hann
• Keywords: Computational Geometry (cs.CG); FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; 02 engineering and technology; Graphics (cs.GR); 3. Good health; Computational Engineering, Finance, and Science (cs.CE); 03 medical and health sciences; Computer Science - Graphics; 0302 clinical medicine; 0202 electrical engineering, electronic engineering, information engineering; Computer Science - Computational Geometry; Computer Science - Computational Engineering, Finance, and Science

6 pages, 6 figures, 1 table, 32 references<br/>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.<br/>