Dice Coefficients (in percentage) of Estimated Truth from Expert Truth with Six Fusion Techniques in 50 Monte Carlos and Discrete STAPLE for Endocardium Identification.

<p>Dice Coefficients (in percentage) of Estimated Truth from Expert Truth with Six Fusion Techniques in 50 Monte Carlos and Discrete STAPLE for Endocardium Identification.</p

Dice Coefficients (in percentage) of Estimated Truth from Expert Truth with Two Informed CSTAPLE Methods Subject to Training Dataset Size Change.

<p>Dice Coefficients (in percentage) of Estimated Truth from Expert Truth with Two Informed CSTAPLE Methods Subject to Training Dataset Size Change.</p

Cardiac MR Image Labeling for Endocardium and RV Insertion Points.

<p>(a) Short-axis MR image of the heart. (b) Pixel labeling of the left ventricle chamber. (c) Labeling of right ventricle insertion points. (d) Level set representation of the left ventricle chamber contour (endocardium).</p

CSTAPLE Simulation of 2-D Point Identification.

<p>In (a) (d) circles are generated truth and dots are rater decisions. In (b) (e) “x” are the fusion of zero initialization, crosses are average initialization, and dots are informed initialization. In (c) (f) “x” are fusion of weak prior, crosses are data-adaptive prior, and dots are informed prior.</p

Identification of RV Insertion Points in Cardiac MRI.

<p>In (a) (b) red dots are rater decisions and green circles are an expert’s decision as ground truth. Fusions are shown in yellow, where “X”, crosses and dots are respectively zero, average, informed initialization in (a) and weak, data-adaptive, informed prior in (b). The error distance of all fusion points from corresponding truth points shown in (c) which compares data-adaptive prior and informed prior methods.</p

Dice Coefficients (in percentage) of Estimated Truth from Expert Truth with Six Fusion Techniques in 50 Monte Carlos and Discrete STAPLE for Endocardium Identification.

<p>Dice Coefficients (in percentage) of Estimated Truth from Expert Truth with Six Fusion Techniques in 50 Monte Carlos and Discrete STAPLE for Endocardium Identification.</p

Poor Bias Initialization in Continuous Label Fusion.

<p>(a) Six raters identify RV insertion points (dots) and their fusions (crosses) are poor because CSTAPLE is initialized in upper left corner. Six raters identify endocardium contours (three shown in (b) and three more shown in (c)). The fusion of six endocardium contours shown in (d) is poor because CSTAPLE was initialized with zeros on the entire image plane.</p

Quantitative CT Imaging of Ventral Hernias: Preliminary Validation of an Anatomical Labeling Protocol

<div><p>Objective</p><p>We described and validated a quantitative anatomical labeling protocol for extracting clinically relevant quantitative parameters for ventral hernias (VH) from routine computed tomography (CT) scans. This information was then used to predict the need for mesh bridge closure during ventral hernia repair (VHR).</p><p>Methods</p><p>A detailed anatomical labeling protocol was proposed to enable quantitative description of VH including shape, location, and surrounding environment (61 scans). Intra- and inter-rater reproducibilities were calculated for labeling on 18 and 10 clinically acquired CT scans, respectively. Preliminary clinical validation was performed by correlating 20 quantitative parameters derived from anatomical labeling with the requirement for mesh bridge closure at surgery (26 scans). Prediction of this clinical endpoint was compared with similar models fit on metrics from the semi-quantitative European Hernia Society Classification for Ventral Hernia (EHSCVH).</p><p>Results</p><p>High labeling reproducibilities were achieved for abdominal walls (±2 mm in mean surface distance), key anatomical landmarks (±5 mm in point distance), and hernia volumes (0.8 in Cohen’s kappa). 9 out of 20 individual quantitative parameters of hernia properties were significantly different between patients who required mesh bridge closure versus those in whom fascial closure was achieved at the time of VHR (p<0.05). Regression models constructed by two to five metrics presented a prediction with 84.6% accuracy for bridge requirement with cross-validation; similar models constructed by EHSCVH variables yielded 76.9% accuracy.</p><p>Significance</p><p>Reproducibility was acceptable for this first formal presentation of a quantitative image labeling protocol for VH on abdominal CT. Labeling-derived metrics presented better prediction of the need for mesh bridge closure than the EHSCVH metrics. This effort is intended as the foundation for future outcomes studies attempting to optimize choice of surgical technique across different anatomical types of VH.</p></div

Hernia volume reliability.

<p>Hernia volume reliability.</p

Fascial boundaries and bony structures reliability measured by Euclidean distance (ED) of centroids in mm.

<p>Fascial boundaries and bony structures reliability measured by Euclidean distance (ED) of centroids in mm.</p