title: A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.
abstract: On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual con-tours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (< 1 ms) with a satisfying accuracy (Dice = 0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system.
authors.affiliation: Soochow University; Washington University School of Medicine; University of Texas at Austin. fengyuan@suda.edu.cn.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Feng
authors.forename: Iwan
authors.initials: I
authors.lastname: Kawrakow
authors.forename: Jeff
authors.initials: J
authors.lastname: Olsen
authors.forename: Parag J
authors.initials: PJ
authors.lastname: Parikh
authors.forename: Camille
authors.initials: C
authors.lastname: Noel
authors.forename: Omar
authors.initials: O
authors.lastname: Wooten
authors.forename: Dongsu
authors.initials: D
authors.lastname: Du
authors.forename: Sasa
authors.initials: S
authors.lastname: Mutic
authors.forename: Yanle
authors.initials: Y
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D004379:Duodenal Neoplasms / Q000532:radiotherapy*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D007680:Kidney Neoplasms / Q000532:radiotherapy*; D008113:Liver Neoplasms / Q000532:radiotherapy*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods; D001749:Urinary Bladder Neoplasms / Q000532:radiotherapy*