title: RFA-cut: Semi-automatic segmentation of radiofrequency ablation zones with and without needles via optimal s-t-cuts.
abstract: 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<;0.423) between the segmentation results of the subgroups for a Mann-Whitney test. Finally, to the best of our knowledge, this is the first time a segmentation approach for CT scans including the RFA needles is reported and we show why another state-of-the-art segmentation method fails for these cases. Intraoperative scans including an RFA probe are very critical in the clinical practice and need a very careful segmentation and inspection to avoid under-treatment, which may result in tumor recurrence (up to 40%). If the decision can be made during the intervention, an additional ablation can be performed without removing the entire needle. This decreases the patient stress and associated risks and costs of a separate intervention at a later date. Ultimately, the segmented ablation zone containing the RFA needle can be used for a precise ablation simulation as the real needle position is known.
authors.forename: Jan
authors.initials: J
authors.lastname: Egger
authors.forename: Harald
authors.initials: H
authors.lastname: Busse
authors.forename: Philipp
authors.initials: P
authors.lastname: Brandmaier
authors.forename: Daniel
authors.initials: D
authors.lastname: Seider
authors.forename: Matthias
authors.initials: M
authors.lastname: Gawlitza
authors.forename: Steffen
authors.initials: S
authors.lastname: Strocka
authors.forename: Philip
authors.initials: P
authors.lastname: Voglreiter
authors.forename: Mark
authors.initials: M
authors.lastname: Dokter
authors.forename: Michael
authors.initials: M
authors.lastname: Hofmann
authors.forename: Bernhard
authors.initials: B
authors.lastname: Kainz
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Chen
authors.forename: Alexander
authors.initials: A
authors.lastname: Hann
authors.forename: Pedro
authors.initials: P
authors.lastname: Boechat
authors.forename: Wei
authors.initials: W
authors.lastname: Yu
authors.forename: Bernd
authors.initials: B
authors.lastname: Freisleben
authors.forename: Tuomas
authors.initials: T
authors.lastname: Alhonnoro
authors.forename: Mika
authors.initials: M
authors.lastname: Pollari
authors.forename: Michael
authors.initials: M
authors.lastname: Moche
authors.forename: Dieter
authors.initials: D
authors.lastname: Schmalstieg
mesh_terms: D055011:Ablation Techniques / Q000295:instrumentation*; D000465:Algorithms; D006528:Carcinoma, Hepatocellular / Q000000981:diagnostic imaging* / Q000601:surgery*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging* / Q000601:surgery*; D009339:Needles; D011846:Radio Waves; D012008:Recurrence; D014057:Tomography, X-Ray Computed