Statistical fusion of continuous labels: identification of cardiac landmarks

Xing, Fangxu; Soleimanifard, Sahar; Prince, Jerry L.; Landman, Bennett A.

doi:10.1117/12.877884

Cited by 9 publications

(15 citation statements)

References 11 publications

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“…STAPLE has been applied successfully in the validation of human brain images (Liu et al, 2007; Archip et al, 2007), consensus guidelines in prostate MRI (Hwee et al, 2011) and observer reliability study in pelvic MRI (Hoyte et al, 2011). In cardiac applications, a preliminary work to identify cardiac landmarks by using STAPLE was reported in (Xing et al, 2011). …”

Section: Introductionmentioning

confidence: 99%

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

Suinesiaputra

Cowan

Al-Agamy

et al. 2014

Medical Image Analysis

166

121

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A collaborative framework was initiated to establish a community resource of ground truth segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising 95 patients (73 men, 22 women; mean age 62.73 ± 11.24 years) with coronary artery disease and prior myocardial infarction, were randomly selected from data made available by the Cardiac Atlas Project (Fonseca et al., 2011). Three semi- and two fully-automated raters segmented the left ventricular myocardium from short-axis cardiac MR images as part of a challenge introduced at the STACOM 2011 MICCAI workshop (Suinesiaputra et al., 2012). Consensus myocardium images were generated based on the Expectation-Maximization principle implemented by the STAPLE algorithm (Warfield et al., 2004). The mean sensitivity, specificity, positive predictive and negative predictive values ranged between 0.63-0.85, 0.60-0.98, 0.56-0.94 and 0.83-0.92, respectively, against the STAPLE consensus. Spatial and temporal agreement varied in different amounts for each rater. STAPLE produced high quality consensus images if the region of interest was limited to the area of discrepancy between raters. To maintain the quality of the consensus, an objective measure based on the candidate automated rater performance distribution is proposed. The consensus segmentation based on a combination of manual and automated raters were more consistent than any particular rater, even those with manual input. The consensus is expected to improve with the addition of new automated contributions. This resource is open for future contributions, and is available as a test bed for the evaluation of new segmentation algorithms, through the Cardiac Atlas Project (www.cardiacatlas.org).

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Section: Introductionmentioning

confidence: 99%

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

Suinesiaputra

Cowan

Al-Agamy

et al. 2014

Medical Image Analysis

166

121

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“…Before these data can be fused, in correspondence to our previously reported method called MAP-CSTAPLE [6] where both the continuous STAPLE algorithm (CSTAPLE) [4, 5] and prior information on rater (training atlas) performance (bias and variance) are needed, we still need to establish a prior model of rater performance. So now we only consider the training set having the centers of mass of all labeled objects.…”

Section: Methodsmentioning

confidence: 99%

“…And also for each rater m , the performance parameter, i.e., bias μ m , and variance Σ m is to be estimated simultaneously. Mathematically, it can be proved that under the same maximum likelihood estimation framework as discrete STAPLE, CSTAPLE yields equal likelihood for arbitrary rater bias parameter μ m so that bias is indeterminate [6] . If a rater has a systematic bias, the resulting fusion may not be accurate if this bias is not accounted.…”

Section: Methodsmentioning

confidence: 99%

“…It has become increasingly common to use human raters to establish an initial atlas of validated training data from which a fully-automated, multi-atlas algorithm is established to mimic the human raters to carry out the desired task on an otherwise prohibitively large dataset. The statistical fusion of object labels carried over from multiple registrations of a set of labeled atlases has been well explored and is being actively investigated and improved [3, 4, 5, 6] . In this paper, we explore the statistical fusion of seeds that are intended to initialize a segmentation algorithm that would run in the native subject space.…”

Section: Introductionmentioning

confidence: 99%

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Finding seeds for segmentation using statistical fusion

et al. 2012

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Image labeling is an essential step for quantitative analysis of medical images. Many image labeling algorithms require seed identification in order to initialize segmentation algorithms such as region growing, graph cuts, and the random walker. Seeds are usually placed manually by human raters, which makes these algorithms semi-automatic and can be prohibitive for very large datasets. In this paper an automatic algorithm for placing seeds using multi-atlas registration and statistical fusion is proposed. Atlases containing the centers of mass of a collection of neuroanatomical objects are deformably registered in a training set to determine where these centers of mass go after labels transformed by registration. The biases of these transformations are determined and incorporated in a continuous form of Simultaneous Truth And Performance Level Estimation (STAPLE) fusion, thereby improving the estimates (on average) over a single registration strategy that does not incorporate bias or fusion. We evaluate this technique using real 3D brain MR image atlases and demonstrate its efficacy on correcting the data bias and reducing the fusion error.

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“…When operating under the assumption that the raters performing the segmentations are collectively unbiased and independent, these algorithms increase the accuracy of a single labeling by probabilistically fusing multiple less accurate delineations. These statistical approaches have been widely used in atlas-fusion techniques [17-19] and have been extended to handle continuous (scalar or vector) images [20-22]…”

Section: Introductionmentioning

confidence: 99%

Robust Statistical Label Fusion Through Consensus Level, Labeler Accuracy, and Truth Estimation (COLLATE)

Asman

Landman

2011

IEEE Trans. Med. Imaging

115

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Segmentation and delineation of structures of interest in medical images is paramount to quantifying and characterizing structural, morphological, and functional correlations with clinically relevant conditions. The established gold standard for performing segmentation has been manual voxel-by-voxel labeling by a neuroanatomist expert. This process can be extremely time consuming, resource intensive and fraught with high inter-observer variability. Hence, studies involving characterizations of novel structures or appearances have been limited in scope (numbers of subjects), scale (extent of regions assessed), and statistical power. Statistical methods to fuse data sets from several different sources (e.g., multiple human observers) have been proposed to simultaneously estimate both rater performance and the ground truth labels. However, with empirical datasets, statistical fusion has been observed to result in visually inconsistent findings. So, despite the ease and elegance of a statistical approach, single observers and/or direct voting are often used in practice. Hence, rater performance is not systematically quantified and exploited during label estimation. To date, statistical fusion methods have relied on characterizations of rater performance that do not intrinsically include spatially varying models of rater performance. Herein, we present a novel, robust statistical label fusion algorithm to estimate and account for spatially varying performance. This algorithm, COnsensus Level, Labeler Accuracy and Truth Estimation (COLLATE), is based on the simple idea that some regions of an image are difficult to label (e.g., confusion regions: boundaries or low contrast areas) while other regions are intrinsically obvious (e.g., consensus regions: centers of large regions or high contrast edges). Unlike its predecessors, COLLATE estimates the consensus level of each voxel and estimates differing models of observer behavior in each region. We show that COLLATE provides significant improvement in label accuracy and rater assessment over previous fusion methods in both simulated and empirical datasets.

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Statistical fusion of continuous labels: identification of cardiac landmarks

Cited by 9 publications

References 11 publications

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

Finding seeds for segmentation using statistical fusion

Robust Statistical Label Fusion Through Consensus Level, Labeler Accuracy, and Truth Estimation (COLLATE)

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