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Average Rating. Submit Review Submit Review. Check Delivery Status. Dispatched in working days. Availability In Stock. This ensured completeness of the atrial wall surrounding the LA. In the MRI dataset, all three imaging planes were taken into consideration. In some slices, it was not possible to determine the border between the left atrium wall and the aortic root wall. In these instances, the entire border between the LA and the aortic root was included, as introducing such a separation in this region would be highly subjective.
Segmentations from each algorithm were compared with the reference standard for atrial wall. As no single metric is advocated as the best metric, three different types of metric were chosen for evaluating the segmentations. These were segmentation overlap, distance and volume-based measures, and they are briefly described below:.
The Dice overlap D is a metric for measuring the degree of overlap in segmentations. It calculates the proportion of true positives in the segmentation as follows:. The LAWT at every pixel location on the outer boundary of the wall was calculated in both the algorithm and consensus ground-truth segmentations. As wall segmentation contours from different algorithms and ground truth are bound to vary, they could not be compared at a pixel level. However, averaging them over slices enabled comparison at the slice level i. The thickness averaged over an entire slice or region R was used as a metric for assessing the accuracy of regional thickness from the segmentations.
The regions considered were posterior and anterior sections of the LA. Additionally, individual slices in the LA axial orientation was also considered. The Euclidean distance d. The thickness T R was then given by:. The total volume was calculated in each segmentation and converted into tissue mass M using the average human myocardial tissue density of 1. An evaluation of how the algorithms handled artefacted regions in the images was important to understand whether they can be utilised in images of sub-optimal quality. Pacing leads and wires in the coronary arteries of patients who have undergone cardiac resynchronisation therapy CRT generate metallic streaks due to its titanium and platinum construction.
The images used in this database were not free from artefacts, there was one image in the database with a CRT pacing wire and two images were of poor quality compared to the other images. For objectively evaluating each algorithm, they were evaluated firstly on images of variable quality and secondly on slices with a pacing wire artefact. In each category, the LAWT measured by the algorithm and ground-truth were compared. A statistical measure known as Pearson Correlation coefficient CC was used to test and measure the linear dependence between LAWT measurements made by the algorithm and ground truth.
The evaluation metrics chosen could only provide isolated rankings. A ranking system was necessary for designing a fair and problem-specific challenge. There are a number of segmentation challenges in literature that provide a ranking schema.
One drawback is that it makes the assumption that expert segmentations are in very close agreement. The final ranking is averaged over all metrics and cases, giving a comprehensive score for each algorithm so it may be ranked. The consensus ground truth for wall segmentation was available for all images on the database.
This allowed the construction of a LAWT atlas. The atlas creation comprised several steps. The LAWT was calculated by projecting normals from each vertex on the 3D surface to the consensus wall segmentation. In the second step, the patient-specific mesh was registered to the mean left atrial anatomical shape using non-rigid registration, bringing the patient-specific LAWT to a common coordinate frame.
In the third and final step, using data in the common frame, the mean LAWT, over all the datasets, could be calculated at every vertex location on the mean left atrium. The atlas was represented on a mean shape. A non-rigid registration was performed between each patient-specific LAWT surface mesh and the mean shape. The registration process comprised both a manual landmark selection step, followed by non-rigid registration of the two surfaces. The non-rigid transformation between two meshes used a free-form deformation between each vertex of the source mesh and the nearest target mesh vertex.
For an illustration of the atlas construction process on the mean shape please see Fig. The steps involved in atlas construction.
Meshes transformed from patient-specific space to a 4-vein anatomical atlas space with non-rigid registration. In atlas space, the thickness is averaged over all cases to generate the final LAWT atlas.
The atlas could be used to predict thickness in new cases. This was demonstrated by registering the atlas to new cases and propagating thickness from the atlas to the new case. To validate this strategy, a leave-one-out LOT cross validation was performed on the image database. Ten separate atlases were constructed and each validated separately on the image that was excluded from the atlas. The LAWT values from the atlas was propagated to the image using the nearest neighbour approach.
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The mean LAWT atlas was obtained as a 3D surface with every vertex on the surface containing a mean thickness value. In the 2D representation, the whole atlas could be visualised simultaneously on a single plane.
The atrium was divided into left, right, roof, anterior and posterior sections. The flat map representation was also sub-divided into the respective sections. The mean thickness in each section was determined and compared to values reported in the literature. The evaluated algorithms generated binary segmentations of the atrial wall from which the wall thickness could be derived.
A sample of the segmentations obtained from the algorithms are illustrated in Fig. The segmentations are analysed, compared and ranked in the following sections. From the CT image database. Each row represents a separate case. The arrows indicate some regions where the wall has clear boundaries.