title: Automated Computed Tomography-Ultrasound Cross-Modality 3-D Contouring Algorithm for Prostate.
abstract: A novel fully automated algorithm is introduced for 3-D cross-modality image segmentation of the prostate, based on the simultaneous use of co-registered computed tomography (CT) and 3-D ultrasound (US) images. By use of a Gabor feature detector, the algorithm can outline in three dimensions and in cross-modality the prostate, and it can be trained and optimized on specific patient populations. We applied it to 16 prostate cancer patients and evaluated the conformity between the automatically segmented prostate contours and the contours manually outlined by an experienced physician, on the CT-US fusion, using the mean distance to conformity (MDC) index. When only the CT scans were used, the average MDC value was 4.5 ± 1.7 mm (maximum value = 9.0 mm). When the US scans also were considered, the mean ± standard deviation was reduced to 3.9 ± 0.7 mm (maximum value = 5.5 mm). The cross-modality approach acted on all the largest distance values, reducing them to acceptable discrepancies.
authors.affiliation: Datamind, Udine, Italy.
authors.forename: Denis
authors.initials: D
authors.lastname: Ermacora
authors.affiliation: Tecnologie Avanzate Srl, Torino, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Pesente
authors.affiliation: Tecnologie Avanzate Srl, Torino, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Pascoli
authors.affiliation: Datamind, Udine, Italy.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Raducci
authors.affiliation: Datamind, Udine, Italy.
authors.forename: Rudy
authors.initials: R
authors.lastname: Mauro
authors.affiliation: Radiation Oncology Department, CRO-Istituto Nazionale Tumori, Aviano (PN), Italy.
authors.forename: Imad Abu
authors.initials: IA
authors.lastname: Rumeileh
authors.affiliation: Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands.
authors.forename: Frank
authors.initials: F
authors.lastname: Verhaegen
authors.affiliation: Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands; Oncology Solutions Department, Philips Research, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands. Electronic address: davide.fontanarosa@maastro.nl.
authors.forename: Davide
authors.initials: D
authors.lastname: Fontanarosa
mesh_terms: D000368:Aged; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007558:Italy; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D064847:Multimodal Imaging / Q000379:methods*; D009426:Netherlands; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014463:Ultrasonography / Q000379:methods*
keywords: Automated image segmentation; Computed tomography imaging; Cross-modality; Image-guided radiation therapy; Radiotherapy; Ultrasound imaging