title: Structured patch model for a unified automatic and interactive segmentation framework.
abstract: We present a novel interactive segmentation framework incorporating a priori knowledge learned from training data. The knowledge is learned as a structured patch model (StPM) comprising sets of corresponding local patch priors and their pairwise spatial distribution statistics which represent the local shape and appearance along its boundary and the global shape structure, respectively. When successive user annotations are given, the StPM is appropriately adjusted in the target image and used together with the annotations to guide the segmentation. The StPM reduces the dependency on the placement and quantity of user annotations with little increase in complexity since the time-consuming StPM construction is performed offline. Furthermore, a seamless learning system can be established by directly adding the patch priors and the pairwise statistics of segmentation results to the StPM. The proposed method was evaluated on three datasets, respectively, of 2D chest CT, 3D knee MR, and 3D brain MR. The experimental results demonstrate that within an equal amount of time, the proposed interactive segmentation framework outperforms recent state-of-the-art methods in terms of accuracy, while it requires significantly less computing and editing time to obtain results with comparable accuracy.
authors: Department of Electrical Engineering, ASRI, INMC, Seoul National University, Seoul, Republic of Korea. Electronic address: shpark13135@gmail.com.
authors: Sang Hyun
authors: SH
authors: Park
authors: Department of Electronic Engineering, Soonchunhyang University, Asan-si, Republic of Korea. Electronic address: soochahn.lee@gmail.com.
authors: Soochahn
authors: S
authors: Lee
authors: Department of Digital Information Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea. Electronic address: yun@hufs.ac.kr.
authors: Il Dong
authors: ID
authors: Yun
authors: Department of Electrical Engineering, ASRI, INMC, Seoul National University, Seoul, Republic of Korea. Electronic address: sanguk@ipl.snu.ac.kr.
authors: Sang Uk
authors: SU
authors: Lee
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Adaptive prior; Incremental learning; Interactive segmentation; Markov random field; Structured patch model