The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images. Issue 4 (17th March 2015)

• Record Type: Journal Article
• Authors: Korsager, Anne Sofie; Fortunati, Valerio; van der Lijn, Fedde; Carl, Jesper; Niessen, Wiro; Østergaard, Lasse Riis; van Walsum, Theo
• Is Part Of: Medical physics. Volume 42:Issue 4(2015)
• Page Start: 1614
• Page End: 1624
• Publication Date: 2015-03-17
• Subjects: biomedical MRI -- cancer -- graph theory -- image fusion -- image registration -- image segmentation -- medical image processing -- radiation therapy; Magnetic resonance imaging -- Segmentation -- Registration -- Combinatorics; graph theory -- Cancer; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Radiation therapy -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; atlas‐based segmentation -- prostate cancer -- image‐guided radiotherapy -- prostate segmentation; Medical image segmentation -- Medical magnetic resonance imaging -- Cancer -- Data sets -- Spatial analysis -- Magnetic resonance -- Vesicles -- Dosimetry; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4914379
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image‐guided radiotherapy treatment of prostate cancer. Methods: A spatial prior based on intersubject atlas registration is combined with organ‐specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T 2 ‐weighted MR images in a leave‐one‐out cross validation experiment and compared with both manual reference segmentations and with multiatlas‐based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas‐based segmentation and the new graph cut method that combines atlas and intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework. Results: A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved. Conclusions: This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.