Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning

Abstract

Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining.

Fully convolutional neural networks improve abdominal organ segmentation

“…However, manual delineation is typically tedious and time consuming. To alleviate the required manual efforts, previous techniques have been made to perform automatic abdominal organ segmentations for computed tomography (CT) [2–6] and magnetic resonance imaging (MRI) [7–10]. However, segmenting these organs presents unique challenges in that the shape, size, location, and orientation of abdominal organs vary greatly from person to person and even from time to time in the same person (as illustrated in Figure 1) [9].…”

“…Additionally, the appearance of those organs depends greatly on the quality of the image, which differs for each scanner. Traditionally, multi-atlas methods have been able to segment abdominal organs with reasonable accuracy [1, 5, 6, 10, 11]. More recently, researchers have demonstrated that fully convolutional neural networks (FCNN) show great promise in both general image segmentation and abdominal organ segmentation of CT scans [1, 12, 13].…”

A competitive strategy for atrial and aortic tract segmentation based on deformable models

“…Initially, several authors extended their individual structure methods to multi-structures. Some examples are the atlasbased (Okada et al, 2015;Wolz et al, 2013;Xu et al, 2015), and statistical-based approaches (Yan et al, 2005;Yang et al, 2004). Despite the high versatility obtained, overlapping and merged regions were typically found, requiring post-processing techniques through mathematical morphology operations or refinement methodologies (Iglesias and Sabuncu, 2015).…”

Multiatlas Segmentation Using Robust Feature-Based Registration

“…Furthermore, another variant of multiatlas segmentation of abdominal organs was used in a recent paper by Xu et al [36]. Atlas selection and label fusion were done using a reformulation of the selective and iterative method for performance-level estimation (SIMPLE) method.…”