Analysis of CMR images within an integrated healthcare framework for remote monitoring

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1 Analysis of CMR images within an integrated healthcare framework for remote monitoring Abstract. We present a software for analyzing Cardiac Magnetic Resonance (CMR) images. This tool has been developed in a framework of a European Project aiming to provide the physicians with support for treatment monitoring and for taking appropriate actions in both the clinical and home environments. The software includes modules for the detection, segmentation and estimation of quantitative parameters of scars and for a functional analysis of the heart. Keywords: Image processing; Cardiac Magnetic Resonance; 1 Introduction The image processing tools we are presenting in this paper have been developed in a framework of a European Project which aims to provide the physicians with support for treatment monitoring and management, and for taking appropriate actions in both the clinical and home environments. One of the main objectives of this project is to follow a new approach in healthcare, from diagnosis and treatment of patients based on symptoms to early diagnosis of patients based on risk assessment of healthy persons. As case study, patients with congestive heart failure are considered, and will be monitored at home with a series of sensors and, if needed, diagnostic Cardiac Magnetic Resonance (CMR) imaging will be performed at hospital. The imaging tools on CMR images are useful to determine the health situation of the patients and to provide a correlation between monitoring at home and the medical imaging chain in the hospital, as illustrated in Figure 1. In fact, the imaging modality can indicate which functions/parameters should be measured by sensors on the patient for the monitoring at home and the sensors can generate alarm and define more precisely what to look for at the hospital with the medical imaging chain. What to measure? Reading room alarm Monitoring at home Hospital or Emergency CMR images What to look for? PACS Fig. 1. Potential role of the CMR image analysis module in a remote monitoring system.

2 2 Materials and methods The CMR images used in this work was collected in a clinical environment. The dataset consists of CMR images of more than sixty patients. All scans were performed with a dedicated 1.5 Tesla Avanto MRI system (Siemens Medical Systems; Erlangen, Germany). Short-axis late gadolinium enhancement images were acquired using a 3D segmented inversion recovery fast gradient echo sequence in 2 breath-holds. Steadystate free precession dynamic gradient-echo mode was used to acquire images during 10 to 15 sec breath-holds. Cine-loops were obtained in 11 to 12 short-axis slices, from the atrio-ventricular ring to the apex (6-7 mm slice thickness, no gaps) with a temporal resolution of 20 frames per cardiac cycle. For each patient endo and epicardial contours were manually traced on each image and at least one scar was manually annotated by doctors. These data were used as the ground truth to test the detection and segmentation algorithms. 2.2 CMR analysis The main goal of the developed software is the usage of delayed-enhancement CMR images to extract important information about the damage in myocardium. This kind of image allows detecting easily the location of the scar and, also, characterizing its principal features. We aim to build a computer tool that could assist the clinician in identifying and quantifying the extent of the cardiac scar in the images, and which can achieve a segmentation and functional analysis of the left and right ventricles. In figure 2 a flow chart of the principal features of the software is illustrated. Fig. 2. Work flow of the analysis implemented for the CMR images. 2.3 Detection of the scars We developed a semiautomatic procedure for the detection of the scars in CMR images. In fact, the user must provide an initial seed within the blood pool (one seed

3 for patient). Our software is based on a Support Vector Machine (SVM) classifier, which is able to detect scars, on the basis of some features of the objects segmented in the image. The training of the systems has been achieved by using images manually annotated by doctors. Our software can mainly be divided in two major stages: - First, some image processing steps are accomplished for reducing the searching area. Here, the idea is to select from the image the regions where scars are usually located. We then segmented objects within this region. - In the second step we estimated some features on those objects and we then make use of these features to classify objects as SCAR or NO SCAR. As starting point for the first stage a seed provided by the user within the ventricle (blood pool) is needed. The choice of this seed is not very critical, every point within the blood pool can be used as starting point and we need only one seed for patient. We then used a region growing algorithm with which, starting from this seed, we grow the neighboring region. In this way we are able to segment the left ventricle. We then performed some morphological operations for removing the spurious signals and calculated the minimal bounding circle of the segmented object for each slice. In this way we have an estimate of the external surface of the ventricle. In order to choose a region where scars are located and reducing at the same time the area of the image to be analyzed, we then calculated an inner and an outer radius, starting from the minimal bounding circle. The annulus between those two circles is the region where we will look for the scars. Local threshold is then achieved for extracting the brighter objects within this region. For all the objects extracted within the selected region, we then estimated a set of features to be provided as inputs to the classifier. We considered both features based on grey-level such as average grey level and entropy, and features connected to the morphology (area, eccentricity, solidity, perimeter, ). Finally, we make use of an SVM classifier, in order to classify the objects segmented during the first described stage, by means of the considered features. 2.4 Segmentation of the scars All the methodologies used for the scar segmentation are based on the functional information obtained, at least, in one slice of the classifier. Among the methods used, the best results have been achieved using level set and watersheds filtering [1,2]. Level set is a numerical method for tracking the evolution of contours and surfaces. The main advantages of level sets is that arbitrarily complex shapes, as in the scar, can be modeled and topological changes such as merging and splitting are handled implicitly. The main advantages of using level sets is that arbitrarily complex shapes can be modeled and topological changes such as merging and splitting are handled implicitly. Using image-based features in the governing differential equation this method can be used to segment the scar. The previous detected scar contour is used as initialization for the level set. Level set based filters have been tested using different features to segment the scar. The most important has been the mean intensity. In watershed algorithm, the image is seen such as a topographic surface where every pixel grey level is considerate like its altitude of the relief. This algorithm could considerate image objects, having the same grey level like a table with the same altitude and recognize edges that divide these objects. Applying a gradient filter and

4 using the gradient magnitude instead of grey scale values, the tables will be seen like basins or concave regions. If we ideally build a dam in the edges of an object the water will fill the basin. The dam defines the separation between different segmented objects called watersheds. Finally is possible to label this watershed to recognize them to the neighbours and improve visual identification. To improve the results and to speed up the executions, watershed algorithm is applied only over the region of interest defined during the pre-process. The label of the watershed corresponding to the scar tissue is selected using the scar detected during the scar analysis step [3,4]. 2.5 Quantification and visualization In order to show the segmentation results we developed a viewer with three MPRs views and a three-dimensional viewer. Multiple data can be loaded at once in order to combine the information provided by the different sources (the original dataset, the myocardium segmented and the scar). In the three-dimensional visor all the data isolated or fused can be shown. Thus, the doctor can visually inspect the scar or the myocardium volumes alone and then to fuse the information into a single volume. Different color palettes could be applied to each volume in order to improve this data analysis. By default, some palettes have been developed to be applied to the scar, the myocardium or the original MRI. In addition, there are tools to create customized palettes by the user. In Figure 3 some examples of results visualization are shown. Fig. 3. Three-dimensional visualization of the myocardium (a), the myocardium and the scar (b) and the original MRI, the myocardium and the scar (c). The volumes are shown fused in a single volume. Once there is an accurate segmentation of the scar, regarding to assist in diagnosis, it is very interesting the extraction of some quantification measures. Extracting the contour surface of the myocardium and the scar, it is possible to compute mathematical or physical analysis such as eccentricity, total surface and mechanical or electrical behavior. 2.6 Ventricle segmentation and functional parameters The first step of this procedure is the automatic detection of the position of the heart chambers in the 3D data. Left ventricle (LV) and right ventricle (RV) position was automatically detected by studying pixel intensity variations throughout the cardiac cycle. On the first volume of the acquired sequence corresponding to the End Diastole

5 (ED) frame, and on the mid one, the central short-axis slices are selected (Fig. 4A). A histogram shape-based image thresholding is applied (Fig. 4B) [5] and the binary images are then labeled (Fig. 4C). The difference between them (Fig. 4D) is used to locate the left and right ventricles (Fig. 4E). Then, we apply morphological operators to dilate and grow this binary image and obtain a mask we will apply to the initial data. Consequently, for each anatomical slice at different height we have a different mask. Fig. 4. Procedure steps for heart chambers automatic localization (see text for detail). An active contour model based on Mumford Shah segmentation techniques and the level set method is then applied to detect endocardial contours of LV and RV [6]. The model is not based on an edge-function to stop the evolving curve on the desired boundary and can detect objects whose boundaries are not necessarily defined by gradient or with very smooth boundaries, for which the classical active contour models are not applicable. The result of this region-based segmentation is then refined applying a regularization motion. The segmentation procedure is applied to the original images in the region of interest obtained with the application of each specific mask. LV slices were manually selected for analysis beginning with the highest basal slice where the LV outflow tract was not visible, and ending with the lowest apical slice where the LV cavity was visualized. RV slices were selected for analysis beginning with the first slice where a pulmonary cusp could be identified (the area of the pulmonary cusp was excluded from the volumes) and ending with the last apical slice to contain blood volume. In every slice, LV and RV endocardial contours were manually traced frame-by-frame with the papillary muscles included in the ventricular cavities, by an experienced investigator. This resulted in LV and RV cross-sectional area for each slice over time. For both manual and automated techniques, global LV and RV volumes were computed throughout the cardiac cycle using a disk-area summation method, from which end-diastolic and end-systolic volumes (EDV and ESV, respectively) were obtained as the maximum and minimum volumes and LV EF was calculated as (EDV-ESV)/EDV 100. Statistical analyses were performed and comparisons between automated and manual measurements of EDV, ESV and EF included linear regression and Bland-Altman analyses. The significance of differences between the two techniques was tested using paired t-test. P-values <0.05 were considered significant. In addition, percent discordance between LV volumes

6 obtained by manual tracing and the automated analysis was calculated for each pair of volume curves as the point-by-point sum of absolute differences between the corresponding values, normalized by the point-by-point sum of the manually traced volumes. 3 Results Figure 5 shows an example of the detection results obtained for one of the analyzed patients. In each image there is the scar annotated by doctors (in red), and the objects classified as scar by our software (in white). In this case, all the objects labeled as scar present a remarkable overlap with the true scar. Fig. 5. Example of the outcomes on the 16 slices of one patient. Left: original images. Right: objects detected as scars (in white), and ground truth provided by doctors (in red). The method for segmenting the scars has been tested in about eighty blocks (where each block consists in an acquired 3D volume of data). It is important to take into account that the ground truth provided by the doctors is not an accurate segmentation since it was generated by delineating the scar contour slice per slice, without a previous improvement of the images quality and without three-dimensional information. Thus, the goal will not be to have the most accurate approximation of the ground truth, but a coherent segmentation in base of the knowledge of the principal features of the scar and the information extracted from the whole scar delineated in each case.

7 Fig. 6. Scar segmentation results using Level sets algorithm. Green: area selected by both the doctor and the automatic method; Red: area identified by the automatic method, but not in the manual annotation; Blue: area selected by the doctors, but not detected by the automatic method. As we can see in Figure 6, when there is just one scar in the myocardium, the automatically segmented scar is quite similar to the manually one. To estimate numerically the similarity of the results with the ground truth we will use the Dice index, which describes the percentage of overlap between two regions. As we can see in Table 1, Dice indices corroborate the conclusions extracted from the visual inspection of the results: the automatically segmented scar is comparable to the manually segmented one, but since the automatic algorithm is 3D and the scar region is not clearly defined in most cases, this similarity is around the fifty percent. Table 1: Dice Index in a population of 83 blocks. Dice Index Mean 0.47 Standard deviation 0.13 Maximum 0.75 Minumum 0.17 Figure 7 shows an example of the LV and RV endocardial contours detected at one phase of the cardiac cycle at different levels from LV apex to base. Importantly, papillary muscles and trabeculae were automatically included in the blood pool. Volume-time curves obtained in one patient by manual tracing and by the automated technique are shown in Figure 8. Linear regression analysis between the automated technique and the manual reference volume values at ED and End Systole (ES) resulted in very good correlation coefficients and regression slopes of for both LV and RV volumes (LV: r=0.99, y=0.98x+2; RV: r=0.99, y=0.87x+15). High correlation and regression slope were also obtained for LV EF (r=0.98, y=0.95x+0.004). Bland- Altman analysis showed no significant biases between the automated measurements and the manual reference technique for LV and RV volumes and EF (bias: -0.2ml; - 0.8ml; -2%). These biases reflected systematic errors of -0.1%, -0.6% and -6% of the corresponding mean values. The 95% limits of agreement were relatively narrow (LV: 12ml; RV: 17ml; EF: 5.5%), providing additional support to the tight agreement between the two techniques. The calculated percent discordance was only 3.7±0.7% for LV volume-time curves and 3.2±1.4% for RV volume-time curves.

8 Fig. 7. Example of the detected LV and RV endocardial contours in one frame from the apex (upper left) to the base (bottom right). Fig. 8. Example of LV (left panel) and RV (bottom panel) volume time curves obtained in one patient by manual tracing and custom software. References 1. Bankman I. (2000). Handbook of Medical Imaging, Processing and Analysis, Ed. Academic Press. 2. Ciofolo C., Fradkin M. (2008), "Segmentation of Pathologic Hearts in Long-Axis Late- Enhancement MRI". MICCAI, Vol. 1, pp Goldenberg R., Kimmel R., Rivlin E. and Rudzsky M. (2005). "Techniques in Automatic Cortical Gray Matter Segmentation of Three-Dimensional (3D) Brain Images". Methods in Cardiovascular and Brain Systems, Vol. 5, pp Landini L., Positano V. and Santarelli M.F. (2007) "3D Medical Image Processing". Image Processing in Radiology. pp Otsu, N.: A threshold selection method from gray-level histograms, IEEE Trans. Sys. Man. Cyber. 9(1): (1979). 6. Chan, T.F., Vese, L.A.: Active Contours Without Edges, IEEE Trans. Image Process. 10(2): (2001).

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