Table 1.
Overview of available post-processing tools. Requirement 1 (R1): myocardial segmentation, requirement 2 (R2): statistical analysis and visualisation following the AHA 17-segment model.
Fig 1.
CMRSegTools architecture diagram.
The plug-in has access to the DICOM viewer functionalities through the Application Program Interface (API) exposed by OsririX/Horos. The DICOM viewer runs on MacOS operating system. List of the frameworks and toolkits in each layer: ITK, Insight Toolkit; VTK, Visualisation Toolkit; DCMTK, DICOM Toolkit; Cocoa, Mac OSX Native AP; OpenGL, Computer Graphics API.
Fig 2.
The CMRSegTools GUI (left) shows the histogram highlighting the bins matching the lesion segmentation. The user can select the algorithms for the segmentation of the myocardium or some of its regions. The image viewer (right) displays the segmented area in magenta as well as the Epicardium (green) and Endocardium (red) contours, LV/RV junction landmark (yellow segment) and AHA region segments. The LGE image presented was obtained with a 3D IR GRE sequence.
Fig 3.
CMRSegTools segmentation output.
(a) Image viewer before executing CMRSegTools; (b) Epicardium and endocardium contours from the myocardial segmentation, lesion segmentation (pixels in magenta colour), histogram and quantification statistics without MVO region; (c) Segmented regions, histogram and quantification statistics after defining the MVO region (contour and pixels in yellow colour).
Fig 4.
Performance of BEAS segmentation on images with different contrast type.
(a) User initialisation on the LGE image; (b) first and (c) second iteration of the automatic segmentation based on (a). (d) User initialisation on the EGE image and (e) segmentation output. (f) Segmentation output on cine bSSFP. All images were taken at the same slice location and from the same patient at different breath-hold scans.
Fig 5.
Example of numerical phantom generation (c), (d), (e) and (f) from a real clinical data set (a) and (b). Images were acquired with a 3D-IR-GRE sequence on a patient with a myocardial infarct. (a) Traced endocardial and epicardial contours on one slice (2D), (b) manual lesion segmentation, (c) output synthetic image (2D). The figures (d), (e) and (f) illustrate transmural extent and ESL calculation on a numerically generated infarct with known size and variable transmural extent around a user-defined mean value. (d) Red, green and yellow contours are respectively the endocardial and epicardial borders, and the mean simulated transmural extent; (e) final synthetic image. (f) CMRSegTools segmentation output (HMRF-EM); magenta pixels on the synthetic image correspond to those classified as myocardial infarct on the numerical phantom. The orange wipers delineate the ESL.
Fig 6.
Performance of the lesion segmentation methods across different Contrast-to-Noise Ratio (CNR) scenarios.
The curves show the relative error (RE) and bars the absolute relative error (ARE, logarithmic scale) for each segmentation method. The CNR was calculated as the ratio between the SI difference of the infarct and healthy myocardium over the standard deviation of the SI in the myocardium.
Fig 7.
Comparison between the manual segmentation made by an expert (without the definition of the MVO region), and the segmentation made by each of the methods within CMRSegTools.
For each segmentation, the figure shows: epicardium (green) and endocardium (red) contours from the myocardial segmentation, lesion segmentation (magenta), remote healthy myocardium (turquoise), no-reflow region by HMRF (yellow), histogram, quantification statistics and relative error.
Fig 8.
Comparison between the manual segmentation made by an expert (including a manual definition of the MVO region), and the segmentation made by each of the methods within CMRSegTools.
For each segmentation, the figure shows: epicardium (green) and endocardium (red) contours from the myocardial segmentation, lesion segmentation (magenta), remote healthy myocardium (turquoise), no-reflow region (yellow), histogram, quantification statistics and relative error.