Virtual M-Mode for Echocardiography: A New Approach for the Segmentation of the Anterior Mitral Leaflet.

Rheumatic heart disease can result from repeated episodes of acute rheumatic fever, which damages the heart valves and reduces their functionality. Early manifestations of heart valve damage are visible in echocardiography in the form of valve thickening, shape changing and mobility reduction. The quantification of these features is important for a precise diagnosis and it is the main motivation for this work. The first step to make this quantification is to accurately identify and track the anterior mitral leaflet throughout the cardiac cycle. An accurate segmentation and tracking with minimum user interaction is still an open problem in literature due to low image quality, speckle noise, signal dropout and nonrigid deformations. In this work, we propose a novel approach for the identification of the anterior mitral valve leaflet in all frames. The method requires a single user-specified point on the posterior wall of the aorta as input, in the first frame. The echocardiography videos are converted into a new image space, the Virtual M-mode, which samples the original echocardiography image over automatically estimated scanning lines. This new image space not only provides the motion pattern of the posterior wall of the aorta, the anterior wall of the aorta and the posterior wall of the left atrium, but also provides the location of the structures in each frame. The location information is then used to initialize the localized active contours, followed by segmenting the anterior mitral leaflet. Results shown that the new image space has robustly identified the anterior mitral valve leaflet, without any failure. The median modified Hausdorff distance error of the proposed method was 2.3 mm, with a recall of 0.94.

A New Method for the Anterior Mitral Leaflet Segmentation in Echocardiography Videos using the Virtual M-mode Space.

Rheumatic heart disease is responsible for the heart valve damage, caused by repeated episodes of rheumatic fever. The disease commonly inflames and scars the mitral valve of the heart, resulting in thicker, less mobile leaflets, with associated decrease in cardiac efficiency. It is important to measure and quantity the early manifestations of this disease, including variations of the thickness, shape and mobility of the leaflets. These manifestations are visible in an echocardiographic screening process. The first step towards the defined objective is to segment the anterior mitral leaflet throughout the cardiac cycle, enabling the future automatic quantification of mentioned clinical parameters. In this work, a new algorithm for the segmentation of the whole region of the anterior mitral leaflet in the virtual M-mode space is proposed. The algorithm requires a single initialization point on the posterior wall of the aorta, in the first frame of the video. A junction point is then computed, showing the location where the two leaflets connect. This junction point helps to automatically redefine the range of virtual M-mode images required to completely segment the region of the anterior mitral leaflet. The segmented anterior mitral leaflet region in the virtual M-mode space is transferred back to regular image space and its shape refined using localized active contours. Results suggest the suitability of the proposed algorithm for the segmentation of anterior mitral leaflet with a median Dice Similarity Coefficient of 0.63, and with median precision and recall of 58% and 73% respectively.

Extracting Thickness Profiles of Anterior Mitral Leaflets in Echocardiography Videos.

Rheumatic heart disease is the serious consequence of repeated episodes of acute rheumatic fever. It is the major cause of heart valve damage resulting in morbidity and mortality. Its early detection is considered vital to control the disease's progression. The key manifestations that are visible in the early stages of this disease are changes in the thickness, shape and mobility of the mitral valve leaflets. Echocardiography based screening is sensitive enough to identify these changes in early stages of the disease. In this work, an automatic approach is proposed to measure, quantify and analyze the thickness of the anterior mitral leaflet, in an echocardiographic video. The shape of the anterior mitral leaflet is simplified via morphological skeletonization and spline modelling to get the central line of the leaflet. To analyze the overall thickness from the tip to its base, the anterior mitral leaflet is divided into four quartiles. In ach quartile the thickness is measured as the length of the line segment resulting from the intersection of the contour with the normal direction of the central point of each quartile. Finally, the thickness is analyzed by measuring the variance per quartile, divided by leaflet position (open, straight and closed). The comparison between the normal and pathological leaflets are also presented, exhibiting statistical significant differences in all quartiles, especially near the tip of the leaflet.

Three-Dimensional Planning Tool for Breast Conserving Surgery: A Technological Review.

Breast cancer is one of the most common malignancies affecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome but, in the case of breast cancer, the surgery aesthetic result has become an important quality indicator for breast cancer patients. In this sense, an adequate surgical planning and prediction tool would empower the patient regarding the treatment decision process, enabling a better communication between the surgeon and the patient and a better understanding of the impact of each surgical option. To develop such tool, it is necessary to create complete 3D model of the breast, integrating both inner and outer breast data. In this review, we thoroughly explore and review the major existing works that address, directly or not, the technical challenges involved in the development of a 3D software planning tool in the field of breast conserving surgery.

Tracking large Anterior Mitral Leaflet displacements by incorporating optical flow in an active contours framework.

Echocardiography is an important tool to detect early evidence of mitral valve degradation associated with rheumatic heart disease. The segmentation and tracking of the Anterior Mitral Leaflet helps to quantify the morphologic valve anomalies, such as the leaflet thickening, shape and the mobility changes. The tracking of this leaflet throughout the cardiac cycle is still an open challenge in the research community. The widely used active contours segmentation framework fails when faced with large leaflet displacement. In this work, we propose the integration of optical flow in an open-ended active contour framework to address this difficulty. This additional information promotes solutions with contours next to high leaflet displacements, resulting in superior performance. The algorithm was tested on 9 fully annotated real clinical videos, acquired from the parasternal long axis view. The algorithm is compared with our previous work. Results show a clear improvement in situations where the leaflet exhibits large displacement or irregular shapes, with an average error of 4.5 pixels and a standard deviation of 2 pixels.

Tracking of the anterior mitral leaflet in echocardiographic sequences using active contours.

Echocardiography assessment of cardiac valves plays a vital role in the diagnosis of rheumatic heart disease. In the vast majority of cases, the mitral valve gets affected, leading to the thickening of its leaflets that may result in the fusion of their tips. This changes the appearance and reduces the mobility of the leaflets, which also reduce the heart efficiency. Quantifying such parameters provides diagnostic insight. To achieve that, the first step is to identify and then track fast moving leaflets. This work is focused on Anterior Mitral Leaflet (AML) tracking. Open ended active contours are employed in this work by removing its boundary conditions. The external and internal energy of the contour is modified that extend the capture range, improve snake energy and encourages the leftmost end point of the contour to converge on the moving tip of the AML. Results show that contour points are tracked accurately with an average error of 4.9 pixels and a standard deviation of 2.1 pixels in 9 fully annotated normal sequences of real children clinical assessments.

Segmentation of bones & MCP joint region of the hand from ultrasound images.

Rheumatic arthritis (RA) is an autoimmune disease that causes irreversible damage to joints and other physiological structures. The Metacarpophalangeal (MCP) joint is one of the first regions to suffer alterations. These alterations are visible with high frequency ultrasound devices, which are used to quantify inflammatory activity in the MCP due to RA. The accurate segmentation of the bone surface and the identification of the MCP capsule region remains a challenge in ultrasound image processing. In this article we aim to make a contribution to this problem by incorporating prior knowledge of the bone and joint regions anatomy into our segmentation algorithm. The log Gabor filter is used for speckle noise reduction and to extract ridge-like structures from the images, while the phase is left unchanged. After thresholding, scores are generated, based on the intensities and areas of the resulting regions, enabling the selection of the structure that best matches the bone. Finally, segmented joint bones are processed to calculate the initial seeds of joint capsule region. Experimental results demonstrate the accuracy of the proposed segmentation algorithm. The mean pixel error between the automatic segmentation and the reference images were 4.4 pixel. The bone regions not segmented were, on average, 5.4%.

Applications of simple robots to encourage social receptiveness of adolescents with autism.

This paper reports the preliminary research studies regarding the use of LEGO Mindstorms TM robots as a therapy for autistic young people. Most autistic people hardly communicate in a social environment, living in their own world. This is the first step in a broader project which aims to improve the autistic people social life and also their ability to communicate, using the robot as a social bridge between them and the surrounding environment/people. The target group is 17-19 years old having autism disorder and developmental disability. The trials consists of undemanding plays where the intervention of the autistic person is required, using simple inputs, such as voice commands, pressing a button or clapping hands. The presented work intends to evaluate the reaction of this particular target group to the introduction of a toy-like robot in their classroom daily life. The methodology applied, the detailed description as well as the analysis of the experiments are presented and discussed.