RFA-cut: Semi-automatic segmentation of radiofrequency ablation zones with and without needles via optimal s-t-cuts
| Autor: | Bernd Freisleben, Steffen Strocka, Pedro Boechat, Michael Hofmann, Michael Moche, Wei Yu, Dieter Schmalstieg, Daniel Seider, Tuomas Alhonnoro, Jan Egger, Philip Voglreiter, Matthias Gawlitza, Philipp Brandmaier, Mark Dokter, Alexander Hann, Bernhard Kainz, Xiaojun Chen, Mika Pollari, Harald Busse |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Ablation Techniques
medicine.medical_specialty Carcinoma Hepatocellular Radiofrequency ablation Radio Waves medicine.medical_treatment Image processing law.invention law Recurrence medicine Image Processing Computer-Assisted Humans Segmentation ta113 business.industry Liver Neoplasms Ablation Needles Computer-aided Radiology Tomography Nuclear medicine business Tomography X-Ray Computed Algorithms Ablation zone |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | In this contribution, we present a semi-automatic segmentation algorithm for radiofrequency ablation (RFA) zones via optimal s-t-cuts. Our interactive graph-based approach builds upon a polyhedron to construct the graph and was specifically designed for computed tomography (CT) acquisitions from patients that had RFA treatments of Hepatocellular Carcinomas (HCC). For evaluation, we used twelve post-interventional CT datasets from the clinical routine and as evaluation metric we utilized the Dice Similarity Coefficient (DSC), which is commonly accepted for judging computer aided medical segmentation tasks. Compared with pure manual slice-by-slice expert segmentations from interventional radiologists, we were able to achieve a DSC of about eighty percent, which is sufficient for our clinical needs. Moreover, our approach was able to handle images containing (DSC=75.9%) and not containing (78.1%) the RFA needles still in place. Additionally, we found no statistically significant difference (p |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|